CN120045430A

CN120045430A - User behavior path construction method and system

Info

Publication number: CN120045430A
Application number: CN202510154079.1A
Authority: CN
Inventors: 张世其; 方纪磊; 何萍; 王焕强
Original assignee: Wuzhou Online E Commerce Beijing Co ltd
Current assignee: Wuzhou Online E Commerce Beijing Co ltd
Priority date: 2025-02-11
Filing date: 2025-02-11
Publication date: 2025-05-27

Abstract

The embodiments of the present specification provide a method and system for constructing a user behavior path, wherein the method for constructing a user behavior path includes: obtaining user behavior data associated with a target service project, and obtaining behavior event data and user group data by cleaning the user behavior data; in response to a project detection request submitted for the target service project, extracting target event detail data from the behavior event data according to the user group data; constructing a user behavior path according to the target event detail data, and segmenting the user behavior path into target user behavior paths according to a session segmentation strategy; and constructing path visualization information corresponding to the project detection request based on the target user behavior path.

Description

User behavior path construction method and system

Technical Field

The embodiment of the specification relates to the technical field of data processing, in particular to a user behavior path construction method and system.

Background

With the development of computer and internet technologies, in order to provide a service item with better stability and experience effects for users, in the experience optimization stage of the service item, a fine-grained service item analysis mode needs to be adopted for optimization. Most of the implementation is realized based on the existing buried point combined with manual processing mode, data analysis requirements are arranged through non-technical staff (product staff and operation staff), information such as events, statistical indexes and the like is recorded, the information is submitted to data processing service to calculate results through SQL written by research staff according to the requirements, and then visual information corresponding to the service item is generated through a report tool of the service item, so that optimization operation of the service item is completed by combining the visual information. However, in this process, the requirement adjustment and the new requirement need the intervention of data research and development resources, which correspondingly brings higher cost, and the processing mode is low in flexibility and not friendly to other personnel besides technicians, so an effective scheme is needed to solve the problems.

Disclosure of Invention

In view of this, the embodiments of the present disclosure provide a user behavior path construction method. One or more embodiments of the present specification also relate to a user behavior path construction apparatus, a user behavior path construction system, a computing device, a computer-readable storage medium, and a computer program product that solve the technical drawbacks of the prior art.

According to a first aspect of embodiments of the present disclosure, there is provided a user behavior path construction method, including:

Acquiring user behavior data associated with a target service item, and acquiring behavior event data and user group data by cleaning the user behavior data;

responding to an item detection request submitted for the target service item, and extracting target event detail data from the behavior event data according to the user group data;

constructing a user behavior path according to the target event detail data, and dividing the user behavior path into target user behavior paths according to a session dividing strategy;

And constructing path visualization information corresponding to the project detection request based on the target user behavior path.

According to a second aspect of embodiments of the present specification, there is provided a user behavior path construction apparatus, including:

The acquisition module is configured to acquire user behavior data associated with a target service item, and acquire behavior event data and user group data by cleaning the user behavior data;

An extraction module configured to extract target event detail data from the behavioral event data in accordance with the user group data in response to an item detection request submitted for the target service item;

The segmentation module is configured to construct a user behavior path according to the target event detail data and segment the user behavior path into a target user behavior path according to a session segmentation strategy;

and the construction module is configured to construct path visualization information corresponding to the project detection request based on the target user behavior path.

According to a third aspect of embodiments of the present disclosure, there is provided a system for constructing a user behavior path, including a client and a server, including:

The server side is used for acquiring user behavior data associated with a target service item, and acquiring behavior event data and user group data by cleaning the user behavior data;

The client is used for receiving an item detection request submitted for the target service item and sending the item detection request to the server;

The server side is used for accessing the target database according to the project detection request, extracting target event detail data from the behavior event data according to the user group data, constructing a user behavior path according to the target event detail data, dividing the user behavior path into target user behavior paths according to a session dividing strategy, constructing path visualization information corresponding to the project detection request based on the target user behavior paths, and sending the path visualization information to the client side;

The client is used for receiving and displaying the path visual information.

According to a fourth aspect of embodiments of the present specification, there is provided a computing device comprising:

a memory and a processor;

the memory is configured to store computer-executable instructions that, when executed by the processor, implement the steps of the user behavior path construction method described above.

According to a fifth aspect of embodiments of the present specification, there is provided a computer-readable storage medium storing computer-executable instructions which, when executed by a processor, implement the steps of the user behavior path construction method described above.

According to a sixth aspect of embodiments of the present specification, there is provided a computer program product comprising a computer program or instructions which, when executed by a processor, implement the steps of the user behavior path construction method described above.

In order to reduce cost investment and improve detection flexibility of service items, the user behavior path construction method provided by the embodiment can firstly acquire user behavior data associated with a target service item so as to acquire behavior event data and user group data by cleaning the user behavior data, and complete data standardization under the condition of no additional introduction of buried points. And then, in response to an item detection request submitted for a target service item, extracting target event detail data from the behavior event data according to user group data, so that the extracted target event detail data can be directly used. The user behavior path of the associated target service item is constructed according to the target event detail data, and in the moment, the fact that the user behavior path is not consistent in time is considered, in order to avoid item detection confusion caused by the problem, the user behavior path can be segmented into the target user behavior path according to a session segmentation strategy, and therefore the specific behavior of each user in the target service item can be intuitively and accurately represented through the target user behavior path. And then constructing path visual information corresponding to the project detection request based on the target user behavior path. Under the condition of not introducing extra resources, the construction of the visual information of the completion path can be quickly and accurately realized, so that the downstream optimization and analysis of the target service project are more convenient. The user behavior path construction method is higher in flexibility, and can be adapted to different types of service items, so that the service item optimization efficiency of a service side is effectively improved.

Drawings

FIG. 1 is a schematic diagram of a method for constructing a user behavior path according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of a method for user behavior path construction provided in one embodiment of the present disclosure;

FIG. 3a is a schematic diagram of event detail data storage in a user behavior path construction method according to one embodiment of the present disclosure;

FIG. 3b is a schematic diagram of user behavior path construction in a method for user behavior path construction according to an embodiment of the present disclosure;

FIG. 3c is a schematic diagram of path visualization information in a method for constructing a path of user behavior according to an embodiment of the present disclosure;

FIG. 4 is a process flow diagram of a user behavior path construction method provided by one embodiment of the present disclosure;

FIG. 5 is a schematic diagram of a user behavior path construction device according to an embodiment of the present disclosure;

FIG. 6 is a schematic diagram of a user behavior path construction system according to one embodiment of the present disclosure;

FIG. 7 is a block diagram of a computing device provided in one embodiment of the present description.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. This description may be embodied in many other forms than described herein and similarly generalized by those skilled in the art to whom this disclosure pertains without departing from the spirit of the disclosure and, therefore, this disclosure is not limited by the specific implementations disclosed below.

The terminology used in the one or more embodiments of the specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of the specification. As used in this specification, one or more embodiments and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any or all possible combinations of one or more of the associated listed items.

It should be understood that, although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first may also be referred to as a second, and similarly, a second may also be referred to as a first, without departing from the scope of one or more embodiments of the present description. The term "if" as used herein may be interpreted as "at..once" or "when..once" or "in response to a determination", depending on the context.

Furthermore, it should be noted that, user information (including, but not limited to, user equipment information, user personal information, etc.) and data (including, but not limited to, data for analysis, stored data, presented data, etc.) according to one or more embodiments of the present disclosure are information and data authorized by a user or sufficiently authorized by each party, and the collection, use, and processing of relevant data is required to comply with relevant laws and regulations and standards of relevant countries and regions, and is provided with corresponding operation entries for the user to select authorization or denial.

First, terms related to one or more embodiments of the present specification will be explained.

SQL (Structured Query Language ) is a standard programming language for managing and manipulating relational databases. It allows a user to perform various data operations such as querying, updating, inserting, deleting, etc. SQL is widely used in various database management systems (DBMS), such as MySQL, postgreSQL, SQLite, oracle, microsoft SQL SERVER, etc.

An event is a specific action that occurs when a user interacts with a product, service, or application. These events are typically used to track and analyze the user's usage habits, preferences, and conversion processes to optimize the user experience and improve performance. Such as page exposure, button clicks, tile exposure, etc.

Event attributes-refers to detailed information associated with a particular event that helps to further understand and refine the circumstances under which the user completes the event. Such as operating system, app version number, city, source, etc.

User path is a term that is used specifically to describe a series of steps or interactions that a user may undergo to achieve a particular goal through a product, service, or website. By analyzing and optimizing the user path, the product manager can improve the overall experience of the user, reduce frustration, and increase conversion.

Session refers to a series of interactions by a user with a website, application, or online service over a particular period of time. A session begins when a user enters a website or application, ends when the user leaves or has no further activity for a certain period of time (commonly referred to as a session timeout). Is a basic unit for evaluating user behavior because it reflects the overall experience of a single access by a user.

Holo bins (Hologres) is a one-stop real-time bin that provides data storage and real-time analysis capabilities. Hologres support multiple storage types, including row, column, and column coexistence, and multiple index types, to meet performance requirements in different scenarios. Meanwhile, hologres is compatible with PostgreSQL ecology and supports various expansion and development tools.

MySQL is a relational database management system that uses a relational model to store data, meaning that the data is organized into tables, between which relationships can be established through specific fields (e.g., foreign keys).

In the present specification, a user behavior path construction method is provided, and the present specification relates to a user behavior path construction apparatus, a user behavior path construction system, a computing device, a computer-readable storage medium, and a computer program product, which are described in detail in the following embodiments one by one.

Referring to the schematic diagram shown in fig. 1, in order to reduce cost investment and improve detection flexibility of service items, the method for constructing a user behavior path according to the present embodiment may first obtain user behavior data associated with a target service item, so as to obtain behavior event data and user group data by cleaning the user behavior data, and complete data normalization without additional introduction of buried points. And then, in response to an item detection request submitted for a target service item, extracting target event detail data from the behavior event data according to user group data, so that the extracted target event detail data can be directly used. The user behavior path of the associated target service item is constructed according to the target event detail data, and in the moment, the fact that the user behavior path is not consistent in time is considered, in order to avoid item detection confusion caused by the problem, the user behavior path can be segmented into the target user behavior path according to a session segmentation strategy, and therefore the specific behavior of each user in the target service item can be intuitively and accurately represented through the target user behavior path. And then constructing path visual information corresponding to the project detection request based on the target user behavior path. Under the condition of not introducing extra resources, the construction of the visual information of the completion path can be quickly and accurately realized, so that the downstream optimization and analysis of the target service project are more convenient. The user behavior path construction method is higher in flexibility, and can be adapted to different types of service items, so that the service item optimization efficiency of a service side is effectively improved.

Referring to fig. 2, fig. 2 shows a flowchart of a method for constructing a user behavior path according to an embodiment of the present disclosure, which specifically includes the following steps.

Step S202, user behavior data associated with a target service item is obtained, and behavior event data and user group data are obtained by cleaning the user behavior data.

The user behavior path construction method provided by the embodiment can be applied to any service item, such as an item for providing logistics service to a user, an item for providing shopping service to a user, an item for providing video watching service to a user, an item for providing transaction service to a user and the like, and by processing user behavior data associated with a target service item, when the service item optimization requirement is met, the user behavior path can be extracted based on the user behavior data so as to construct path visualization information in combination with the user behavior path, so that a service party can know the relevant optimization direction of an associated service item in combination with the path visualization information, such as user behavior intention, flow source analysis, loss point identification, abnormal path detection, user behavior difference, user feature comparison and the like, and further the optimized service item can provide high-quality service to the user.

In this embodiment, the target service item is taken as an example of a logistics service item, the user behavior path construction method is described, and descriptions of other service scenarios about the user behavior path construction method can be referred to the same or corresponding descriptions in this embodiment, which are not repeated here.

Specifically, the target service item is an item pointing to a service provided by a user, and is borne by an application program, a webpage or an applet installed in a terminal device, so that the user can participate in the target service item by using the application program, the webpage or the applet, and further obtain a project service provided by the target service item, such as a logistics function, a transaction function, a shopping function, a query function, a translation function, a video browsing function, a text reading function and the like. The user behavior data specifically refers to behavior data corresponding to users participating in a target service item, and is used for recording each behavior operation performed by each user when the users participate in the target service item, for example, the users use logistics service to enter an application program, select a logistics function, input a single number and other series of operations to form the user behavior data, and the user behavior data contains attribute data of behavior actions, such as time, operation modes, called functions and the like, besides the data of the behavior actions, and is used for accurately constructing a behavior path of each user by combining the user behavior data later so as to embody path visual information of the target service item and facilitate the optimized use of the service item at the downstream.

Correspondingly, the behavior event data specifically refers to event description data representing user behavior operation obtained by cleaning user behavior data, and the corresponding user group data specifically refers to record data corresponding to different groups obtained by dividing users participating in a target service project according to different requirements, and the record data is used for extracting relevant event data by combining the user group data after receiving a project detection request, so that a user behavior path corresponding to the project detection request is constructed.

Based on the method, in order to reduce cost investment and improve detection flexibility of the service items, user behavior data associated with the target service items can be acquired first, so that behavior event data and user group data can be obtained by cleaning the user behavior data, and data normalization can be completed without additionally introducing buried points. And then, in response to an item detection request submitted for a target service item, extracting target event detail data from the behavior event data according to user group data, so that the extracted target event detail data can be directly used. The user behavior path of the associated target service item is constructed according to the target event detail data, and in the moment, the fact that the user behavior path is not consistent in time is considered, in order to avoid item detection confusion caused by the problem, the user behavior path can be segmented into the target user behavior path according to a session segmentation strategy, and therefore the specific behavior of each user in the target service item can be intuitively and accurately represented through the target user behavior path. And then constructing path visual information corresponding to the project detection request based on the target user behavior path. Under the condition of not introducing extra resources, the construction of the visual information of the completion path can be quickly and accurately realized, so that the downstream optimization and analysis of the target service project are more convenient. The user behavior path construction method is higher in flexibility, and can be adapted to different types of service items, so that the service item optimization efficiency of a service side is effectively improved.

Further, in order to enable the behavior event data and the user group data to be directly used when the project detection request is received, the user behavior data can be cleaned so as to obtain standardized behavior data, and then the behavior event data and the user group data are constructed. In this embodiment, the specific implementation manner is as follows:

The method comprises the steps of carrying out data cleaning on user behavior data according to preset cleaning rules to obtain target user behavior data, wherein the user behavior data are related to different data sources of a target service item, determining event detail data, event metadata, user data and user attribute data according to the target user behavior data, taking the event detail data and the event metadata as behavior event data, and taking the user data and the user attribute data as user group data.

Specifically, the cleaning rule specifically refers to a rule for cleaning user behavior data related to a target service item to meet subsequent use requirements, and the rule can enable user behavior data corresponding to different data sources of the target service item to be converted into target user behavior data with the same standard, so that subsequent item detection analysis is performed according to the target user behavior data with the same standard. Correspondingly, the event detail data specifically refers to description data corresponding to each event generated when each user participates in the target service item, and the event metadata specifically refers to description data of each type of event, for example, the event detail data records behavior event details of clicking an "A" control by different users, and the event metadata can record behavior events of clicking the "A" control by a large number of users as one metadata, so that the target event detail data can be conveniently queried in an application stage. Correspondingly, the user data specifically refers to description data corresponding to different user groups which are marked, the user attribute data specifically refers to attributes corresponding to different user groups, for example, a target service item is provided with n participating users, different users can be selected according to one or more conditions of region, gender, old and new, age, occupation, liveness and the like, further different user groups are obtained, and the type of each user group can be described through the attribute data.

Based on the above, in order to convert the user behavior data associated with the target service item into data which is convenient for constructing a user behavior path, and support the subsequent receiving of the item detection request, the corresponding detail data is quickly extracted for analysis. The user behavior data can be cleaned according to a preset cleaning rule to obtain target user behavior data, so that the user behavior data of different data sources can be unified to obtain the target user behavior data. On the basis, event detail data and event metadata, user data and user attribute data can be determined according to target user behavior data, the event detail data and the event metadata can be used as behavior event data, and the user data and the user attribute data can be used as user group data at the moment, so that the behavior event detail of each user is embodied through the event detail data, each event type is embodied through the event metadata, meanwhile, different user groups including the number of users are embodied through the user data, and the types of different user groups are embodied through the user attribute data, and therefore project optimization and use are facilitated.

In sum, by combining the event detail data and the event metadata to construct the behavior event data and combining the user data and the user attribute data to construct the user group data, the behavior event data and the user group data can represent the global item of the target service item, so that the follow-up use in construction of the user behavior path is more convenient.

Furthermore, in order to support the subsequent use when determining the event metadata and the event detail data, the event metadata may be determined in a deduplication manner, so as to improve the subsequent data query efficiency. In this embodiment, the specific implementation manner is as follows:

Determining event detail data according to the target user behavior data, de-duplicating the event detail data to obtain initial event metadata, performing semantic processing on the initial event metadata, and generating event metadata according to semantic processing results, wherein the event detail data is stored in an event detail table of the target service item, and the event metadata is stored in an event metadata table of the target service item.

Specifically, the initial event metadata specifically means that after the event detail data is obtained, considering that each event detail data corresponds to a behavior event, and a large amount of event detail data may have the same behavior operation, so that only one type of finally obtained event detail data can be left by repeating the event detail data, at this time, the type of the remaining event detail data is taken as the initial event metadata, and the event metadata is not subjected to semantic processing yet, and cannot define a name for the event detail data. Correspondingly, the semantic processing is carried out on the initial event metadata, in particular to the processing operation of naming the metadata of which the event type is difficult to define, and the event metadata which is not repeated and can express the type attribute is obtained by carrying out semantic analysis on the event metadata. Correspondingly, the event detail table specifically refers to a data table for recording event detail data, which can be realized through Holo bins. Accordingly, the event metadata table specifically refers to a data table for recording event metadata, which may be implemented through a MYSQL database.

Based on the method, in order to enable the behavior event data to meet the subsequent use requirement, event detail data can be determined according to target user behavior data, initial event metadata with type representation meanings can be obtained through de-duplication of the event detail data, on the basis, the event code is considered to be generally used for identifying events through the event code in an original selling point, the concrete meaning of the event is difficult to understand, therefore semantic processing can be conducted on the initial event metadata, the event metadata can be generated according to semantic processing results, naming of each type of event through the semantic event is achieved, the event can be named as a button 1 click through the semantic event, further efficiency is higher when the event detection processing is conducted later, the event detail data is stored in an event detail table of a target service item, and the event metadata is stored in an event metadata table of the target service item.

In practical applications, event metadata may be recorded in an event metadata table through a data model, and event detail data may be recorded in an event detail table through an offline data model, for example, a table name of the event detail table is { dwd _user_ behavior _event_detail_di__ $ { tenantId }, where the recorded content is as follows (1):

Field name	Field type	Description of the invention	Whether or not to be empty
				log_id	string	Log ID	Whether or not
event_code	string	Event code	Whether or not
				local_timestamp	bigint	Client local timestamp	Whether or not
...	...	...	...
				data_channel	int	Data collection channel	Whether or not

(1)

On the basis, the event detail data and the event metadata of the target service project can be subjected to persistence so as to be used in the project detection stage.

For example, in a product providing a logistics service, in order to facilitate product optimization, event data related to the logistics service may be collected and stored in an optimization stage. As shown in the schematic diagram in fig. 3a, after the user behavior raw data corresponding to the logistics service item is collected through the buried point 1 (such as a preset code point in an application program or a web page for collecting user behavior data), the buried point 2 (such as a basic data collecting point in a user behavior tracking system), and the buried point 3 (such as a code segment preset in the application program or the web page for collecting and analyzing data of user behavior, system performance, etc.), the user behavior raw data of different data sources can be consolidated into unified user behavior data through a cleaning rule. And synchronizing the event metadata to an event metadata table MYSQL storing the global event metadata of the related logistics service item, and simultaneously, semantically processing the newly added event metadata in the event metadata table MYSQL to name the event metadata which is difficult to understand in specific meaning, thereby being convenient for subsequent use.

Furthermore, in order to facilitate optimization of the logistics service project by the downstream service, the user and the user attribute corresponding to the logistics service project can be selected by the circling platform so as to obtain different user groups and the user attribute corresponding to different user groups, such as an A area male group, an A area female group, a group with ages of 18-20 years old, a group with ages of more than 25 years old and the like, so that event detail data can be extracted by the subsequent multiplexing of the selected groups for logistics service optimization.

In summary, by processing the user behavior data of the target service item to obtain user group data of the corresponding user and behavior event data of the corresponding event, the required detail data can be quickly selected when the user behavior path is constructed in response to the item detection request later, so that the item detection efficiency is effectively improved.

And step S204, in response to the project detection request submitted for the target service project, extracting target event detail data from the behavior event data according to the user group data.

Specifically, after the processing and persistence of the user behavior data of the target service item are completed, if an item inspection request submitted for the target service item is received, it is indicated that the target service item needs to be analyzed according to the item inspection request at this time, and then path visualization information matching the item inspection request is constructed, so that the downstream optimizes the target service item according to the path visualization information. Before that, event detail data of the associated project detection request needs to be extracted for subsequent analysis, so in order to ensure the accuracy and efficiency of data extraction, target event detail data can be extracted from the behavior event data according to user group data in response to the project detection request submitted for the target service project, the extracted target event detail data can be ensured to meet the detection requirement of the project detection request, and then path visualization information matched with the project detection request is constructed for optimizing and analyzing the target service project at the downstream.

The project detection request is specifically a request submitted when the pointer optimizes and analyzes the target service project, and the request includes, but is not limited to, a path detection request, a traffic detection request, a churn detection request, an abnormal path detection request, a behavior difference detection request, and the like, and is used for carrying out different types of detection processing on the target service project. The target event detail data specifically refers to event detail data meeting the current project detection requirement is screened out of the recorded behavior event data according to user group data in response to a project detection request, and can be understood as detection requirement parameters related to project detection request records, user groups and event screening parameters can be determined according to the parameters, and the target event detail data can be extracted from the behavior event data by combining the information of the user groups and the event screening parameters so as to be used for carrying out detection processing operation corresponding to the project detection request subsequently.

Further, when the target event detail data is extracted in response to the item detection request, the behavior event data is considered to contain global event detail data of the target service item, so that the target group sub-data can be determined first, then the target event metadata is determined, and further the target event detail data is accurately hit, thereby improving the data extraction efficiency. In this embodiment, the specific implementation manner is as follows:

the method comprises the steps of receiving a project detection request submitted for a target service project, obtaining detection parameters by analyzing the project detection request, screening target group sub-data in user group data according to the detection parameters, determining target event metadata in the behavior event data according to the target group sub-data, and extracting target event detail data in the behavior event data based on the target event metadata.

Specifically, the detection parameters specifically refer to parameters used for screening data meeting the detection requirements of the item in the item detection request, and include, but are not limited to, session time, event information, participation event, time information, screening range information, and the like, and are used for ensuring that the screened target event detail data meets the current detection requirements of the item. Correspondingly, the target group sub-data specifically refers to description data corresponding to the user group meeting the current project detection requirement in the user group data. Accordingly, the target event metadata specifically refers to event metadata for filtering event detail data, which meets the detection requirement of the current project.

Based on the above, after receiving the project detection request submitted for the target service project, in order to be able to extract the target event detail data meeting the current project detection requirement in response to the project detection request, the project detection request may be parsed first to obtain the detection parameters, and the required data range may be determined by the detection parameters. Then, target group sub-data can be firstly screened from the user group data according to the detection parameters; and then determining target event metadata in the behavior event data according to the target group sub-data, further determining the type covered by the event detail data, and finally extracting the target event detail data from the behavior event data based on the target event metadata so as to facilitate the subsequent construction of path visualization information for the optimized use of the target service item.

Along the above example, after the event detail data and the event metadata are stored in the data table, the data in the data table can be used for the optimized use of the subsequent logistics service project. When a detection request for a logistics service item is received, parameters such as a session event, a start event, a stop event, a participation event, a user group and the like can be determined by analyzing the detection request, then an event metadata table MYSQL can be accessed according to the parameters to determine hit metadata, then the metadata is used for accessing an event detail table Holo, and then event detail data corresponding to a user hit by the detection request can be read in the event detail table. In this embodiment, a user behavior path construction method will be described by taking the example that the hit user includes user 1 and user 2. Specifically, the obtained target event detail data is shown in the following table (2):

user id (uid)	Event code (event_code)
		1	a
1	b
		1	c
1	c
		1	d
1	a
		2	a
2	b
		2	b

(2)

Where 1 and 2 represent user 1 and user 2, and a, b, c, d represent the names of specific operational events performed by the user in the logistics application. After the target event detail data corresponding to the detection requirement is obtained, the subsequent path construction processing can be performed.

In conclusion, the extraction of the target event detail data is carried out by combining the detection parameters in the project detection request, and the extraction process has more standard path operation, so that the extraction precision of the event detail data can be effectively improved, and the subsequent processing efficiency can be effectively improved.

Step S206, constructing a user behavior path according to the target event detail data, and dividing the user behavior path into target user behavior paths according to a session dividing strategy.

Specifically, after the target event detail data corresponding to the project detection request is obtained, in order to reflect the requirement result corresponding to the current project detection request through the target event detail data, the target event detail data needs to be processed, so that the target event detail data can be presented to a user in a visual expression mode. Therefore, a user behavior path can be constructed according to the target event detail data, and behavior operation of a user meeting the project detection request when participating in a target service project can be reflected through the user behavior path, so that subsequent analysis and use are facilitated. Meanwhile, considering that paths which affect subsequent analysis and processing, such as abnormal operation with longer session time, possibly exist in the user behavior paths, the user behavior paths can be segmented, and at the moment, the user behavior paths can be segmented into target user behavior paths according to a session segmentation strategy, so that the target user behavior paths can accurately reflect the behavior operation of each user in participating in a target service item, and the subsequent construction of the path visual information is convenient to use.

The user behavior path specifically refers to a path constructed corresponding to user behavior operation according to event detail data of each user recorded in target event detail data. Correspondingly, the session segmentation policy specifically refers to a policy for performing segmentation processing on the user behavior path according to session time, and is used for ensuring that the obtained target user behavior path accords with the actual operation behavior of the user.

Furthermore, when constructing the user behavior path, considering that the target event detail data is the event detail data corresponding to all the defined users, in order to enable the user behavior path of each user to accurately embody the behavior operation of each user, the user behavior path can be realized in a grouping and constructing mode, and on the basis, because different project detection requests need to construct different types of user behavior paths, the user behavior path construction needs to be carried out by combining the types of the project detection requests. In this embodiment, the specific implementation manner is as follows:

Grouping the target event detail data according to the user identification to obtain at least two groups of event detail sub-data;

In the first aspect, when the item detection request is a subsequent behavior detection request, ascending sort is performed on the at least two groups of event detail sub-data respectively, initial event information corresponding to the subsequent behavior detection request is determined, and a user behavior path is constructed according to the initial event information and the at least two groups of event detail sub-data after ascending sort.

In the second aspect, when the project detection request is a previous behavior detection request, descending order sorting is respectively carried out on the at least two groups of event detail sub-data, termination event information corresponding to the previous behavior detection request is determined, and a user behavior path is constructed according to the termination event information and the at least two groups of event detail sub-data after descending order sorting.

The method comprises the steps of determining at least two groups of event detail sub-data, wherein the at least two groups of event detail sub-data specifically comprise a group of data structures formed by each piece of event detail data corresponding to each user, corresponding follow-up behavior detection requests specifically comprise requests for analyzing follow-up events related to the follow-up events after the start events in a target service item, corresponding start event information specifically comprise information corresponding to the start events set when follow-up behavior analysis is needed, corresponding follow-up behavior detection requests specifically comprise requests for analyzing follow-up events related to the follow-up events before the end events in the target service item, and corresponding end event information specifically comprise information corresponding to the end events set when the follow-up behavior analysis is needed.

Based on this, as shown in the schematic diagram of fig. 3b, considering that different user behavior path construction is required for the detection requests of different target service items, so as to meet the subsequent optimization processing requirement, the target event detail data may be first grouped according to the user identifier to obtain at least two groups of event detail sub-data, so as to implement grouping of event detail data of each user.

In the first aspect, when the item detection request is a subsequent behavior detection request, it is stated that other subsequent events after the initial event need to be analyzed and processed at this time, so that ascending sort can be performed on at least two groups of event detail sub-data respectively, so that the event detail data contained in each group of event detail sub-data can be arranged in ascending order according to time sequence. On the basis, initial event information corresponding to the subsequent behavior detection request is determined, and a user behavior path is constructed according to the initial event information and at least two groups of event detail sub-data after ascending sequence.

In the second aspect, in the case where the item detection request is a preceding behavior detection request, it is described that other preceding events before the termination event need to be analyzed and processed at this time, so that at least two sets of event detail sub-data may be sorted in descending order, respectively, so that the event detail data included in each set of event detail sub-data may be sorted in descending order according to time sequence. On the basis, determining termination event information corresponding to the previous behavior detection request, and constructing a user behavior path according to the termination event information and at least two groups of event detail sub-data after descending order.

In summary, different event detail sub-data ordering modes are set for different project detection requests, so that the event detail sub-data can be accurately ordered according to requirements, and further, the constructed user behavior path is ensured to accord with actual operation behaviors, so that the constructed user behavior path is conveniently used for path visual information construction.

Further, after the user behavior path is obtained, the problem that the user behavior path does not consider the time sequence problem may affect the subsequent analysis is considered, so in order to avoid the problem, the user behavior path may be segmented in combination with the session segmentation policy. In this embodiment, the specific implementation manner is as follows:

determining session time and event information according to a session segmentation strategy, detecting interval time between adjacent event nodes in the user behavior path, comparing the interval time with the session time, determining a path segmentation position in the user behavior path according to a comparison result, segmenting the user behavior path according to the segmentation position to obtain at least two candidate user behavior paths, and filtering the at least two candidate user behavior paths based on the event information to obtain a target user behavior path.

The session time is specifically a time threshold preset for detecting whether the time between every two events exceeds the normal operation time, the event information is specifically information corresponding to the starting event, the interval time is specifically a time interval between any adjacent event nodes in the user behavior path, and the path dividing position is specifically a position for dividing the user behavior path, wherein the position can divide the user behavior path into at least two candidate user behavior paths.

Based on the above, as shown in the schematic diagram of fig. 3b, after the user behavior path is constructed based on the event detail data, in order to enable the user behavior path to accurately embody the user behavior operation and facilitate the subsequent path visualization information construction, the user behavior path may be subjected to segmentation processing, specifically, the session time and the event information may be determined according to the session segmentation policy, and the interval time between adjacent event nodes in the user behavior path may be detected, at this time, the interval time and the session time may be compared, and the adjacent event nodes greater than the session time may be screened out as path segmentation positions capable of being used for segmenting the user behavior path according to the comparison result, and then the user behavior path may be segmented according to the segmentation positions to obtain at least two candidate user behavior paths.

Along the above example, after obtaining the target event detail data as shown in table (2), the target event detail data may be first grouped according to the user uid, and according to the grouping result, the following tables (3) and (4) are obtained, which are the event detail data corresponding to the user 1 and the event detail data corresponding to the user 2, respectively:

Watch (3)

User id (uid)	Event code (event_code)
		2	a
2	b
		2	b

Watch (4)

In the first aspect, when the detection request of the logistics service item is that the subsequent behavior needs to be analyzed, the tables (3) and (4) may be sorted in ascending order according to ts (user behavior occurrence time), and the following tables (5) and (6) may be obtained according to the sorting result:

user id (uid)	Event code (event_code)
		1	a
1	b
		1	c
1	c
		1	d
1	a

Watch (5)

User id (uid)	Event code (event_code)
		2	a
2	b
		2	b

Watch (6)

On the basis, taking the event a as an initial event according to the detection request, and constructing user behavior paths of the user 1 and the user 2 based on the initial event and the sequenced result, wherein the user behavior path { a- > b- > c- > c- > d- > a } corresponding to the user 1 is obtained, and the user behavior path { a- > b- > b } corresponding to the user 2 is obtained. On the basis, the user behavior paths of the user 1 and the user 2 can be segmented according to the conversation time of 5 minutes, if the time of the adjacent event c in the user behavior path of the user 1 exceeds 5 minutes, the user behavior path can be divided into { a- > b- > c } and { c- > d- > a }, and meanwhile, c- > d in the path { c- > d- > a } can be filtered out due to the fact that the initial event is a, and then the target user behavior paths { a- > b- > c } and { a } corresponding to the user 1 and the target user behavior paths { a- > b- > b } corresponding to the user 2 can be obtained, so that path visualization information can be built by combining the paths later for optimizing use of logistics service items.

On the other hand, when the detection request of the logistics service item is that the preceding behavior needs to be analyzed, the above tables (3) and (4) may be sorted in descending order according to ts (user behavior occurrence time), and the following tables (7) and (8) may be obtained according to the sorting result:

user id (uid)	Event code (event_code)
		1	a
1	d
		1	c
1	c
		1	b
1	a

Watch (7)

User id (uid)	Event code (event_code)
		2	b
2	b
		2	a

Watch (8)

Based on the above, taking the event a as the termination event according to the detection request, and carrying out user behavior path construction of the user 1 and the user 2 based on the start event and the result after the descending order, wherein the processes of user behavior path construction, segmentation and filtering can be referred to the above description, and finally target user behavior paths { a- > d- > c } and { a } corresponding to the user 1 and target user behavior paths { a } corresponding to the user 2 can be obtained, so that path construction path visual information can be combined later for optimal use of the logistics service item.

In summary, by constructing the user behavior paths respectively corresponding to each user and performing to-be-segmented on the user behavior paths, the user behavior paths can precisely embody the operation behaviors of each user, so that the path visualization information meeting the project detection request can be constructed later for use.

Step S208, constructing path visualization information corresponding to the project detection request based on the target user behavior path.

Specifically, after the target user behavior path is obtained, in order to construct visualized information by combining the target user behavior path, so as to be convenient for analyzing the target service item, path visualized information can be constructed by combining the target user behavior path. The path visualization information specifically refers to a path analysis chart which is constructed according to a user behavior path and is convenient for a user to watch, and may be a path destination chart as shown in fig. 3c, or may be another type of chart which is convenient for project optimization, and the embodiment is not limited in any way.

Along the above example, after obtaining the target user behavior paths corresponding to each user respectively, the target user behavior paths corresponding to each user can be clustered and converted according to the detection request, so as to generate a path destination diagram as shown in fig. 3c, so that the service party of the logistics service project can optimize the logistics service according to the diagram, and further provide more stable logistics service for the user.

Further, the constructed path visual information is different for different project detection requests, so that the problem expression of the target service project is different. In this embodiment, the following specifically includes:

In the case that the item detection request is a path detection request, the path visualization information is used for characterizing a participation intention of a user in participating in the target service item;

the path visualization information is used for representing traffic source information of the target service item under the condition that the item detection request is a traffic detection request;

The path visualization information is used for representing the loss node information of the target service item under the condition that the item detection request is a loss detection request;

The path visualization information is used for representing abnormal behavior path information in the target service item under the condition that the item detection request is an abnormal path detection request;

and in the case that the item detection request is a behavior difference detection request, the path visualization information is used for representing behavior difference information associated with the target service item.

The method comprises the steps of determining a path detection request, namely searching whether a user uses a target service item according to a main operation path expected by the design of the target service item, determining a flow detection request, namely analyzing distribution proportion of different flow sources and detection requests of user quality of all sources, determining a loss detection request, namely identifying links with high loss and high loss rate in the use process of the target service item, determining an abnormal path detection request, namely detecting whether an abnormal user behavior path (such as a circulating path or an unexpected path) exists in the target service item, and determining a behavior difference detection request, namely analyzing a difference detection request of the user behavior path under the conditions of different user groups, label classification, equipment types and the like, or comparing characteristic distribution of lost users and successful users to find out a common point and a detection request of a difference point.

That is, the path visualization information may be used to analyze traffic source information of a target service item in case the item detection request is a traffic detection request, to analyze attrition node information of the target service item in case the item detection request is an attrition detection request, to analyze abnormal behavior path information in the target service item in case the item detection request is an abnormal path detection request, and to analyze behavior difference information associated with the target service item in case the item detection request is a behavior difference detection request.

The following describes, with reference to fig. 4, an example of application of the user behavior path construction method provided in the present specification in an analysis scenario of a shopping application program, where the user behavior path construction method is further described. Fig. 4 is a flowchart of a process of a method for constructing a user behavior path according to an embodiment of the present disclosure, which specifically includes the following steps.

Step S402, user behavior data associated with a target service item is obtained, data cleaning is carried out on the user behavior data according to a preset cleaning rule, and the target user behavior data is obtained, wherein the user behavior data are associated with different data sources of the target service item.

Step S404, determining event detail data and event metadata, and user data and user attribute data according to the target user behavior data.

Step S406, event detail data and event metadata are taken as behavior event data, and user data and user attribute data are taken as user group data.

Step S408, a project detection request submitted for the target service project is received, and a detection parameter is obtained by parsing the project detection request.

Step S410, target group sub-data is screened from the user group data according to the detection parameters.

Step S412, determining target event metadata in the behavior event data according to the target group sub-data, and extracting target event detail data in the behavior event data based on the target event metadata.

Step S414, grouping the target event detail data according to the user identification to obtain at least two groups of event detail sub-data.

Step S416, when the item detection request is a subsequent behavior detection request, the ascending sort is performed for at least two groups of event detail sub-data, respectively.

Step S418, determining initial event information corresponding to the subsequent behavior detection request, and constructing a user behavior path according to the initial event information and at least two groups of event detail sub-data after ascending sequence.

Step S420, determining session time and event information according to the session segmentation strategy, and detecting interval time between adjacent event nodes in the user behavior path.

Step S422, the interval time is compared with the session time, and the path dividing position is determined in the user behavior path according to the comparison result.

Step S424, the user behavior paths are segmented according to the segmentation positions, and at least two candidate user behavior paths are obtained.

Step S426, filtering the at least two candidate user behavior paths based on the event information to obtain a target user behavior path.

Step S428, path visualization information corresponding to the project detection request is constructed based on the target user behavior path.

In summary, in order to reduce cost investment and improve detection flexibility of service items, user behavior data associated with target service items may be acquired first, so as to obtain behavior event data and user group data by cleaning the user behavior data, and complete data normalization without additional introduction of buried points. And then, in response to an item detection request submitted for a target service item, extracting target event detail data from the behavior event data according to user group data, so that the extracted target event detail data can be directly used. The user behavior path of the associated target service item is constructed according to the target event detail data, and in the moment, the fact that the user behavior path is not consistent in time is considered, in order to avoid item detection confusion caused by the problem, the user behavior path can be segmented into the target user behavior path according to a session segmentation strategy, and therefore the specific behavior of each user in the target service item can be intuitively and accurately represented through the target user behavior path. And then constructing path visual information corresponding to the project detection request based on the target user behavior path. Under the condition of not introducing extra resources, the construction of the visual information of the completion path can be quickly and accurately realized, so that the downstream optimization and analysis of the target service project are more convenient. The user behavior path construction method is higher in flexibility, and can be adapted to different types of service items, so that the service item optimization efficiency of a service side is effectively improved.

Corresponding to the method embodiment, the present disclosure further provides an embodiment of a user behavior path building device, and fig. 5 shows a schematic structural diagram of the user behavior path building device provided in one embodiment of the present disclosure. As shown in fig. 5, the apparatus includes:

An obtaining module 502 configured to obtain user behavior data associated with a target service item, and obtain behavior event data and user group data by cleaning the user behavior data;

An extraction module 504 configured to extract target event detail data from the behavioral event data in accordance with the user group data in response to an item detection request submitted for the target service item;

A segmentation module 506 configured to construct a user behavior path according to the target event detail data, and segment the user behavior path into a target user behavior path according to a session segmentation policy;

a construction module 508 is configured to construct path visualization information corresponding to the item detection request based on the target user behavior path.

In an alternative embodiment, the obtaining module 502 is further configured to:

In an alternative embodiment, the extraction module 504 is further configured to:

In an alternative embodiment, the segmentation module 506 is further configured to:

the method comprises the steps of grouping target event detail data according to user identification to obtain at least two groups of event detail sub-data, respectively carrying out ascending sort on the at least two groups of event detail sub-data when the project detection request is a follow-up action detection request, determining initial event information corresponding to the follow-up action detection request, and constructing a user action path according to the initial event information and the at least two groups of event detail sub-data after ascending sort.

And determining termination event information corresponding to the prior behavior detection request, and constructing a user behavior path according to the termination event information and the at least two groups of event detail sub-data after the descending order sequencing.

In an alternative embodiment, the apparatus further comprises:

The method comprises the steps of enabling a user to participate in a target service item, enabling path visualization information to be used for representing participation intention of the user in the target service item when an item detection request is a path detection request, enabling the path visualization information to be used for representing flow source information of the target service item when the item detection request is a flow detection request, enabling the path visualization information to be used for representing flow loss node information of the target service item when the item detection request is a flow loss detection request, enabling the path visualization information to be used for representing abnormal behavior path information in the target service item when the item detection request is an abnormal path detection request, and enabling the path visualization information to be used for representing behavior difference information related to the target service item when the item detection request is a behavior difference detection request.

In order to reduce cost investment and improve detection flexibility of service items, the user behavior path construction device provided by the embodiment can firstly acquire user behavior data associated with a target service item, so as to acquire behavior event data and user group data by cleaning the user behavior data, and complete data standardization under the condition of no additional introduction of buried points. And then, in response to an item detection request submitted for a target service item, extracting target event detail data from the behavior event data according to user group data, so that the extracted target event detail data can be directly used. The user behavior path of the associated target service item is constructed according to the target event detail data, and in the moment, the fact that the user behavior path is not consistent in time is considered, in order to avoid item detection confusion caused by the problem, the user behavior path can be segmented into the target user behavior path according to a session segmentation strategy, and therefore the specific behavior of each user in the target service item can be intuitively and accurately represented through the target user behavior path. And then constructing path visual information corresponding to the project detection request based on the target user behavior path. Under the condition of not introducing extra resources, the construction of the visual information of the completion path can be quickly and accurately realized, so that the downstream optimization and analysis of the target service project are more convenient. The user behavior path construction method is higher in flexibility, and can be adapted to different types of service items, so that the service item optimization efficiency of a service side is effectively improved.

The above is a schematic scheme of a user behavior path construction device of the present embodiment. It should be noted that, the technical solution of the user behavior path construction device and the technical solution of the user behavior path construction method belong to the same concept, and details of the technical solution of the user behavior path construction device which are not described in detail can be referred to the description of the technical solution of the user behavior path construction method.

Corresponding to the method embodiment, the present disclosure further provides a user behavior path building system embodiment, and fig. 6 shows a schematic structural diagram of a user behavior path building system provided in one embodiment of the present disclosure. As shown in fig. 6, the user behavior path construction system 600 includes a server 610 and a client 620, including:

the server 610 is configured to obtain user behavior data associated with a target service item, and obtain behavior event data and user group data by cleaning the user behavior data;

the client 620 is configured to receive an item detection request submitted for the target service item, and send the item detection request to the server;

The server 610 is configured to access the target database according to the item detection request, extract target event detail data from the behavior event data according to the user group data, construct a user behavior path according to the target event detail data, and segment the user behavior path into a target user behavior path according to a session segmentation policy;

the client 620 is configured to receive and display the path visualization information.

In order to reduce cost investment and improve detection flexibility of service items, the user behavior path construction system provided by the embodiment can firstly acquire user behavior data associated with a target service item, so as to acquire behavior event data and user group data by cleaning the user behavior data, and complete data standardization under the condition of no additional introduction of buried points. And then, in response to an item detection request submitted for a target service item, extracting target event detail data from the behavior event data according to user group data, so that the extracted target event detail data can be directly used. The user behavior path of the associated target service item is constructed according to the target event detail data, and in the moment, the fact that the user behavior path is not consistent in time is considered, in order to avoid item detection confusion caused by the problem, the user behavior path can be segmented into the target user behavior path according to a session segmentation strategy, and therefore the specific behavior of each user in the target service item can be intuitively and accurately represented through the target user behavior path. And then constructing path visual information corresponding to the project detection request based on the target user behavior path. Under the condition of not introducing extra resources, the construction of the visual information of the completion path can be quickly and accurately realized, so that the downstream optimization and analysis of the target service project are more convenient. The user behavior path construction method is higher in flexibility, and can be adapted to different types of service items, so that the service item optimization efficiency of a service side is effectively improved.

The above is a schematic scheme of a user behavior path construction system of the present embodiment. It should be noted that, the technical solution of the user behavior path construction system and the technical solution of the user behavior path construction method belong to the same concept, and details of the technical solution of the user behavior path construction system which are not described in detail can be referred to the description of the technical solution of the user behavior path construction method.

Fig. 7 illustrates a block diagram of a computing device 700 provided in accordance with one embodiment of the present description. The components of computing device 700 include, but are not limited to, memory 710 and processor 720. Processor 720 is coupled to memory 710 via bus 730, and database 750 is used to store data.

Computing device 700 also includes access device 740, access device 740 enabling computing device 700 to communicate via one or more networks 760. Examples of such networks include public switched telephone networks (PSTN, public Switched Telephone Network), local area networks (LAN, local Area Network), wide area networks (WAN, wide Area Network), personal area networks (PAN, personal Area Network), or combinations of communication networks such as the internet. The access device 740 may include one or more of any type of network interface, wired or wireless, such as a network interface card (NIC, network interface controller), such as an IEEE802.11 wireless local area network (WLAN, wireless Local Area Network) wireless interface, a worldwide interoperability for microwave access (Wi-MAX, worldwide Interoperability for Microwave Access) interface, an ethernet interface, a universal serial bus (USB, universal Serial Bus) interface, a cellular network interface, a bluetooth interface, near Field Communication (NFC).

In one embodiment of the present description, the above-described components of computing device 700, as well as other components not shown in FIG. 7, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device illustrated in FIG. 7 is for exemplary purposes only and is not intended to limit the scope of the present description. Those skilled in the art may add or replace other components as desired.

Computing device 700 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smart phone), wearable computing device (e.g., smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or personal computer (PC, personal Computer). Computing device 700 may also be a mobile or stationary server.

The processor 720 is configured to execute computer-executable instructions that, when executed by the processor, implement the steps of the user behavior path construction method described above.

The foregoing is a schematic illustration of a computing device of this embodiment. It should be noted that, the technical solution of the computing device and the technical solution of the above-mentioned user behavior path construction method belong to the same concept, and details of the technical solution of the computing device, which are not described in detail, can be referred to the description of the technical solution of the above-mentioned user behavior path construction method.

An embodiment of the present disclosure also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the user behavior path construction method described above.

The above is an exemplary version of a computer-readable storage medium of the present embodiment. It should be noted that, the technical solution of the storage medium and the technical solution of the above-mentioned user behavior path construction method belong to the same concept, and details of the technical solution of the storage medium which are not described in detail can be referred to the description of the technical solution of the above-mentioned user behavior path construction method.

An embodiment of the present disclosure further provides a computer program, where the computer program, when executed in a computer, causes the computer to perform the steps of the above-mentioned user behavior path construction method.

The above is an exemplary version of a computer program of the present embodiment. It should be noted that, the technical solution of the computer program and the technical solution of the above-mentioned user behavior path construction method belong to the same concept, and details of the technical solution of the computer program, which are not described in detail, can be referred to the description of the technical solution of the above-mentioned user behavior path construction method.

An embodiment of the present disclosure also provides a computer program product, including a computer program or instructions, which when executed by a processor, implement the steps of the user behavior path construction method described above.

The foregoing is a schematic version of a computer program product of this embodiment. It should be noted that, the technical solution of the computer program product and the technical solution of the above-mentioned user behavior path construction method belong to the same concept, and details of the technical solution of the computer program product, which are not described in detail, can be referred to the description of the technical solution of the above-mentioned user behavior path construction method.

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The computer instructions include computer program code that may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be increased or decreased appropriately according to the requirements of the patent practice, for example, in some areas, according to the patent practice, the computer readable medium does not include an electric carrier signal and a telecommunication signal.

It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the embodiments are not limited by the order of actions described, as some steps may be performed in other order or simultaneously according to the embodiments of the present disclosure. Further, those skilled in the art will appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily all required for the embodiments described in the specification.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.

The preferred embodiments of the present specification disclosed above are merely used to help clarify the present specification. Alternative embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the teaching of the embodiments. The embodiments were chosen and described in order to best explain the principles of the embodiments and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. This specification is to be limited only by the claims and the full scope and equivalents thereof.

Claims

1. A user behavior path construction method comprises the following steps:

2. The user behavior path construction method according to claim 1, the obtaining behavior event data and user group data by washing the user behavior data, comprising:

Performing data cleaning on the user behavior data according to a preset cleaning rule to obtain target user behavior data, wherein the user behavior data are associated with different data sources of the target service item;

Determining event detail data and event metadata, and user data and user attribute data according to the target user behavior data;

the event detail data and the event metadata are taken as behavior event data, and the user data and the user attribute data are taken as user group data.

3. The user behavior path construction method according to claim 2, wherein the determining event detail data and event metadata according to the target user behavior data includes:

Determining event detail data according to the target user behavior data, and performing deduplication on the event detail data to obtain initial event metadata;

Performing semantic processing on the initial event metadata, and generating event metadata according to a semantic processing result;

the event detail data is stored in an event detail table of the target service item, and the event metadata is stored in an event metadata table of the target service item.

4. The user behavior path construction method according to claim 1, wherein the extracting target event detail data from the behavior event data according to the user group data in response to an item detection request submitted for the target service item comprises:

receiving an item detection request submitted for the target service item, and obtaining detection parameters by analyzing the item detection request;

Screening target group sub-data from the user group data according to the detection parameters;

Determining target event metadata in the behavior event data according to the target group sub-data, and extracting target event detail data from the behavior event data based on the target event metadata;

The constructing a user behavior path according to the target event detail data comprises the following steps:

Under the condition that the project detection request is a subsequent behavior detection request, respectively carrying out ascending sort on the at least two groups of event detail sub-data;

and determining initial event information corresponding to the follow-up behavior detection request, and constructing a user behavior path according to the initial event information and at least two groups of event detail sub-data after ascending sequence ordering.

5. The user behavior path construction method according to claim 4, the method further comprising:

under the condition that the project detection request is a previous behavior detection request, respectively sorting the at least two groups of event detail sub-data in a descending order;

And determining termination event information corresponding to the previous behavior detection request, and constructing a user behavior path according to the termination event information and at least two groups of event detail sub-data after descending order sequencing.

6. The user behavior path construction method according to claim 1, wherein the dividing the user behavior path into the target user behavior paths according to the session division policy comprises:

Determining session time and event information according to a session segmentation strategy, and detecting interval time between adjacent event nodes in the user behavior path;

Comparing the interval time with the session time, and determining a path segmentation position in the user behavior path according to the comparison result;

dividing the user behavior paths according to the dividing positions to obtain at least two candidate user behavior paths;

Filtering the at least two candidate user behavior paths based on the event information to obtain a target user behavior path;

wherein the method further comprises:

7. A user behavior path construction system comprises a client and a server, and comprises:

The client is used for receiving and displaying the path visual information.

8. A computing device, comprising:

a memory and a processor;

The memory is configured to store computer executable instructions, the processor being configured to execute the computer executable instructions, which when executed by the processor, implement the steps of the method of any one of claims 1 to 6.

9. A computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program or instructions which, when executed by a processor, implement the steps of the method of any one of claims 1 to 6.