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WO2018121700A1 - Procédé et dispositif de recommandation d'informations d'application sur la base d'une application installée, dispositif terminal et support de stockage - Google Patents

Procédé et dispositif de recommandation d'informations d'application sur la base d'une application installée, dispositif terminal et support de stockage Download PDF

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Publication number
WO2018121700A1
WO2018121700A1 PCT/CN2017/119610 CN2017119610W WO2018121700A1 WO 2018121700 A1 WO2018121700 A1 WO 2018121700A1 CN 2017119610 W CN2017119610 W CN 2017119610W WO 2018121700 A1 WO2018121700 A1 WO 2018121700A1
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WIPO (PCT)
Prior art keywords
information
user
application
users
similarity
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Application number
PCT/CN2017/119610
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English (en)
Chinese (zh)
Inventor
潘岸腾
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广州优视网络科技有限公司
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Publication of WO2018121700A1 publication Critical patent/WO2018121700A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0623—Item investigation
    • G06Q30/0625—Directed, with specific intent or strategy
    • G06Q30/0627—Directed, with specific intent or strategy using item specifications
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations

Definitions

  • the present application relates to the field of information processing technologies, and in particular, to a method, an apparatus, a terminal device, and a storage medium for recommending application information based on an installed application.
  • the embodiment of the present application provides a method for recommending application information based on an installed application, including:
  • a certain amount of information is selected as candidate information in descending order of matching degree and recommended to the user in order of matching degree from large to small.
  • the user is determined according to the number of users who have clicked the information j among the users who have installed the application i and the number of users who have installed the application i.
  • the similarity between the application i and the information j in the stream library has been installed.
  • the similarity is calculated using the following formula:
  • n the number of applications the user has installed in the N days until today
  • m represents the amount of information in the information flow library
  • N is an integer greater than zero.
  • the matching degree of the user to the information j is determined according to the number of days the user last installed the application i and maintaining the today and the obtained similarity.
  • the degree of similarity obtained is the similarity between the installed application i and the information j in the information flow library.
  • the matching degree is calculated using the following formula:
  • n the number of applications the user has installed in the N days until today
  • m represents the amount of information in the information flow library
  • the user's interest rate i i for the application i is calculated as follows:
  • n the number of applications the user has installed in the N days until today
  • N is an integer greater than zero.
  • the corresponding information is selected from the candidate information according to a predetermined information quality rule and recommended to the user.
  • the comprehensive quality index values calculated by the three parameters are selected according to the order of the comprehensive quality index values from the largest to the smallest.
  • the calculation method of the comprehensive quality index value is:
  • m represents the amount of information in the information flow library
  • c j represents the number of users who clicked on the information j
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the information flow library
  • g j represents the number of users who liked the information j
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the information flow library
  • d j represents the number of users who generate the application behavior by the information j;
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the stream library.
  • the embodiment of the present application further provides an apparatus for recommending application information based on an installed application, including:
  • a similarity determining unit configured to determine a similarity between the installed information of the application and the different information in the information flow library
  • a matching degree determining unit configured to use the obtained similarity degree to obtain a matching degree of the user to different information
  • Selecting units for selecting a certain amount of information as candidate information in order of matching degree from large to small are selected from large to small;
  • a recommendation unit for recommending candidate information to the user in descending order of matching degree is a recommendation unit for recommending candidate information to the user in descending order of matching degree.
  • the similarity determining unit determines the similarity between the user j installed information and the information j in the information flow library according to the number of users who clicked the information j among the users who have installed the application i and the number of users who have installed the application i. .
  • the similarity determining unit calculates the similarity using the following formula:
  • n the number of applications the user has installed in the N days until today
  • m represents the amount of information in the information flow library
  • N is an integer greater than zero.
  • the matching degree determining unit determines the matching degree of the user to the information j according to the number of days the user last installed the application i and maintains today and the obtained similarity, wherein the obtained similarity is the installed application i and the information.
  • the matching degree determining unit calculates the matching degree using the following formula:
  • n the number of applications the user has installed in the N days until today
  • m represents the amount of information in the information flow library
  • the user's interest rate i i for the application i is calculated as follows:
  • n the number of applications the user has installed in the N days until today
  • N is an integer greater than zero.
  • the recommendation unit may be further configured to: select, according to a predetermined information quality rule, the corresponding information from the candidate information to recommend to the user.
  • the recommendation unit according to one of a click rate, a click rate and a conversion rate of the candidate information, or a combination of any two or two parameters, or a comprehensive quality index value calculated according to the three parameters, according to the comprehensive quality
  • the index values are selected from the largest to the smallest, and the corresponding information is recommended to the user.
  • the calculation method of the comprehensive quality index value is:
  • m represents the amount of information in the information flow library
  • c j represents the number of users who clicked on the information j
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the information flow library
  • g j represents the number of users who liked the information j
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the information flow library
  • d j represents the number of users who generate the application behavior by the information j;
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the stream library.
  • the embodiment of the present application further provides a terminal device, including:
  • One or more processors are One or more processors;
  • One or more applications wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to:
  • a certain amount of information is selected as candidate information in descending order of matching degree and recommended to the user in order of matching degree from large to small.
  • the user is determined according to the number of users who have clicked the information j among the users who have installed the application i and the number of users who have installed the application i.
  • the similarity between the application i and the information j in the stream library has been installed.
  • the similarity is calculated using the following formula:
  • n the number of applications the user has installed in the N days until today
  • m represents the amount of information in the information flow library
  • N is an integer greater than zero.
  • the matching degree of the user to the information j is determined according to the number of days the user last installed the application i and maintaining the today and the obtained similarity.
  • the degree of similarity obtained is the similarity between the installed application i and the information j in the information flow library.
  • the matching degree is calculated using the following formula:
  • n the number of applications the user has installed in the N days until today
  • m represents the amount of information in the information flow library
  • the user's interest rate i i for the application i is calculated as follows:
  • n the number of applications the user has installed in the N days until today
  • N is an integer greater than zero.
  • the method further includes: selecting, according to a predetermined information quality rule, the corresponding information from the candidate information to recommend to the user.
  • the comprehensive quality index values calculated by the three parameters are selected according to the order of the comprehensive quality index values from the largest to the smallest.
  • the calculation method of the comprehensive quality index value is:
  • m represents the amount of information in the information flow library
  • c j represents the number of users who clicked on the information j
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the information flow library
  • g j represents the number of users who liked the information j
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the information flow library
  • d j represents the number of users who generate the application behavior by the information j;
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the stream library.
  • the embodiment of the present application further provides a computer readable storage medium carrying one or more computer instruction programs thereon, when the computer instruction program is executed by one or more processors, the one or more processor implementations are based on
  • the method for installing the application to recommend the application information includes: determining the similarity between the installed information of the user and the different information in the information flow library; using the obtained similarity degree to obtain the matching degree of the user to different information; The order of large to small selects a certain amount of information as candidate information and recommends to the user in order of matching degree from large to small.
  • the method and apparatus for recommending application information based on the installed application according to the present application fully considers the interests and hobbies of the user, and determines the matching of different information by the user by determining the similarity between the installed information and the different information in the information flow library. Degree, thereby selecting a certain number of candidate information, may recommend corresponding candidate information according to the degree of matching from large to small, or may select appropriate information from the candidate information according to the information quality indicator to recommend to the user, which can be based on Different application information of different users' different interests and hobbies, so as to achieve personalized recommendation, which greatly enhances the user experience.
  • FIG. 1A is a screenshot of an example of an existing application store recommendation application
  • FIG. 1B is a screenshot of an example of an existing application store adopting an information flow recommendation application
  • Figure 1C is an example screenshot of a detail page of a message of the information flow
  • FIG. 2 is a flowchart of a method for recommending application information based on an installed application according to the first embodiment of the present application
  • Figure 3 is a screenshot of an example of clicking on a message stream to open a corresponding application
  • FIG. 4 is a schematic block diagram of an apparatus for recommending application information based on an installed application according to a second embodiment of the present application
  • FIG. 5 is a block diagram showing the internal structure of a terminal device according to a third embodiment of the present application.
  • FIG. 2 is a flowchart of a method for recommending application information based on an installed application according to the first embodiment of the present application. Assume that a user A clicks on the information flow section of the application store on the terminal that he uses, and needs to recommend a batch of application information to the user A. The recommended application based on the installed application of the present application as shown in FIG. 2 can be used.
  • a method of information the method comprising the steps of:
  • S1 Determine the similarity between the user's installed application and the different information in the information flow library.
  • the installed application here refers to an application that has been installed on the terminal used by the user when recommending the application information to the user.
  • the first step is to determine the similarity between the user's installed application and the different information in the repository.
  • the specific implementation method is as follows:
  • Each application in the app store or app market will contain 1-3 or more tags, which are used to identify the type to which the app belongs, so that the tag of the app can be used to determine the user's interest, ie by tag identification.
  • the user has installed the category of the app to determine the user's interest.
  • the label that the information is promoted by the application can also be used as the type to which the information belongs, as shown in FIG. 3, for example, the application "Himalaya” which is promoted by opening the information, and the application "Himalaya” can be seen.
  • the details of the bottom position are the two tabs "News Reading” and "Listening" of the app.
  • the application is based on whether the method has the same label, and the application information that is highly correlated is found according to the installed application of the user, and then the similarity between the installed information of the application and the different information in the information flow library is calculated.
  • the user has installed the application here, including the application downloaded and installed by the user through the application store or the application market, and the application that can be found in the application store or the application market.
  • the information flow library is preset when the application development store or application market, and the information in the library can be updated from time to time.
  • Having the same label means that the set of labels included in one application i has an intersection with the set of labels contained in another information j.
  • the similarity between the user j installed information and the information j in the information flow library is determined according to the number of users b i, j who have clicked the information j among the users who have installed the application i and the number of users a i have installed the application i. .
  • n the number of applications the user has installed in the N days until today
  • m represents the amount of information in the information flow library
  • N is an integer greater than zero.
  • the meaning of the formula is that when the installed application i and the information j do not have the same label, the similarity is 0; when there is the same label, the similarity is the number of users who clicked the information j among the users who have installed the application i The proportion of the number of users who have installed the application i. The larger the ratio, the higher the similarity.
  • n means that the number of applications that a user keeps installing within N days until today means that, as described above, when a batch of application information is to be recommended to a certain user A, the method is first executed to find recommended information.
  • the number of applications installed on the terminal that the user A is using is counted.
  • the number of installed installed applications is the number of all applications installed on the terminal, preferably refers to the third-party application; of course, N can also be set to a specific limited number of days, for example, 60 days. 90 days and so on.
  • the matching degree of the user A to different information is determined from the multiple applications. In order to find information with high matching.
  • the matching degree of the user to the information j is determined according to the last time the user installs the application i and maintains the number of days t i and the obtained similarity s i,j , wherein the obtained similarity is the installed application i and the information The similarity between the information j in the stream library.
  • the degree of interest o i of the user A to the application i can be obtained according to t i , and then the degree of matching of the user to the information j is determined according to the product of the similarity s i,j and the degree of interest o i .
  • n the number of applications that User A maintains installed within N days until today;
  • m represents the amount of information in the information flow library
  • N is an integer greater than zero.
  • the meaning of the formula is: multiplying the user's interest in the installed application i by the similarity between the application i and the information, and then determining the matching degree of the user to different information by superimposing.
  • n is the same as n in step S1, and no repetitive explanation is made.
  • the user A's interest in the application i is calculated as follows:
  • n the number of applications that User A maintains installed within N days until today;
  • N is an integer greater than 0, which represents the number of days.
  • the meaning of interest o i is that the user's interest will change with time. The closer the user installs the application, the more the user's current interest.
  • n is the same as n in step S1, and no repetitive explanation is made.
  • t i indicates that the user A last installed the application i and maintained the number of days until today means that the user A has been installed on the day when the application i was last installed and has not been uninstalled in the middle but remains in the terminal that the user is using. The number of days until today.
  • S3 Select a certain amount of information as candidate information according to the order of matching degree, and recommend the user to the user in descending order of matching degree.
  • a certain amount of information ranked in front is selected as the candidate information of interest of the user according to the size of the matching degree value. For example, select the corresponding information with the top 50 matching ranks, or the corresponding information ranked in the top 100. The corresponding candidate information can then be recommended to the user in descending order of matching.
  • the method for recommending application information based on the installed application of the present application may further include the following steps:
  • S4 Select corresponding information from the candidate information according to a predetermined information quality rule to recommend to the user.
  • the predetermined information quality rule may be a comprehensive quality indicator value for determining the information, and the comprehensive quality indicator may be one of a click rate, a click rate, and a conversion rate of the candidate information, or may be based on a click rate, a click rate, and a conversion rate.
  • the comprehensive quality index value of the candidate information calculated by any two or two combinations, or the comprehensive quality index value of the candidate information calculated according to the three parameters of click rate, click rate and conversion rate, according to the comprehensive quality index value from large to The small order selects the corresponding information to recommend to the user.
  • the comprehensive quality index value is calculated according to the comprehensive quality index value.
  • the order of the large to small selection of the corresponding information is recommended to the user, wherein the calculation method of the comprehensive quality indicator value is:
  • m represents the amount of information in the information flow library
  • Parameter indicators to calculate the comprehensive quality indicator value are used to calculate the comprehensive quality indicator value;
  • c j represents the number of users who clicked on the information j
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the information flow library
  • g j represents the number of users who liked the information j
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the information flow library
  • d j represents the number of users who generate the application behavior by the information j;
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the stream library.
  • the above formula considers the click rate, the like rate, and the conversion rate of candidate information as factors for measuring the quality of information.
  • the weights of each factor are adjusted by ⁇ , ⁇ , and ⁇ to consider which one or two factors are considered according to practical needs. As a major measure of quality. The following is a detailed description by way of example.
  • the comprehensive quality index of the candidate information is the click rate of the information.
  • the comprehensive quality index of the candidate information is the click rate of the information.
  • the method for recommending application information based on the installed application according to the present application fully considers the interests and hobbies of the user, and determines the degree of matching of different information by the user by determining the similarity between the installed information and the different information in the information flow library, Therefore, a certain number of candidate information is selected, and the corresponding candidate information may be recommended to the user according to the matching degree from the largest to the smallest, or the appropriate information may be selected from the candidate information according to the information quality indicator, and the user may be recommended according to different users.
  • Different application hobbies and different recommended application information to achieve personalized recommendations, which greatly enhances the user experience.
  • FIG. 4 is a schematic block diagram of an apparatus for recommending application information based on an installed application according to a second embodiment of the present application. As shown in FIG. 4, the apparatus for recommending application information based on an installed application of the present application includes:
  • a similarity determining unit configured to determine a similarity between the installed information of the application and the different information in the information flow library
  • a matching degree determining unit configured to use the obtained similarity degree to obtain a matching degree of the user to different information
  • a recommendation unit for recommending candidate information to the user in descending order of matching degree is a recommendation unit for recommending candidate information to the user in descending order of matching degree.
  • the similarity determination unit determines that the user has installed the application i and the information j in the information flow library according to the number of users who have clicked the information j among the users who have installed the application i and the number of users who have installed the application i. Similarity.
  • the similarity determining unit calculates the similarity using the following formula:
  • n the number of applications the user has installed in the N days until today
  • m represents the amount of information in the information flow library
  • N is an integer greater than zero.
  • the matching degree determining unit determines the matching degree of the user to the information j according to the number of days the user last installed the application i and maintains to today and the obtained similarity, wherein the obtained similarity is the installed application i The similarity between the information j and the information j in the information flow library.
  • the matching degree determining unit calculates the matching degree using the following formula:
  • n the number of applications the user has installed in the N days until today
  • m represents the amount of information in the information flow library
  • the degree of user interest for the application of i o i is calculated as follows:
  • n the number of applications the user has installed in the N days until today
  • N is an integer greater than zero.
  • the recommendation unit in the device for recommending application information based on the installed application of the present application may also be used according to
  • the predetermined information quality rule selects the corresponding information from the candidate information and recommends it to the user.
  • the recommendation unit according to one of the click rate, the click rate and the conversion rate of the candidate information, or according to any two or two parameter combinations, or the comprehensive quality index value calculated according to the three parameters,
  • the comprehensive quality index values are selected from the largest to the smallest, and the corresponding information is recommended to the user.
  • the calculation method of the comprehensive quality index value is:
  • m represents the amount of information in the information flow library
  • Parameter indicators to calculate the comprehensive quality indicator value are used to calculate the comprehensive quality indicator value;
  • c j represents the number of users who clicked on the information j
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the information flow library
  • g j represents the number of users who liked the information j
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the information flow library
  • d j represents the number of users who generate the application behavior by the information j;
  • f j represents the number of all users who have presented the information j to the user
  • m represents the amount of information in the stream library.
  • the device for recommending application information based on the installed application according to the present application fully considers the interests and hobbies of the user, and determines the degree of matching of the different information by the user by determining the similarity between the installed information and the different information in the information flow library, Therefore, a certain number of candidate information is selected, and the corresponding candidate information may be recommended to the user according to the matching degree from the largest to the smallest, or the appropriate information may be selected from the candidate information according to the information quality indicator, and the user may be recommended according to different users. Different application hobbies and different recommended application information to achieve personalized recommendations, which greatly enhances the user experience.
  • the present application provides a terminal device as a third embodiment, which specifically includes:
  • One or more processors are One or more processors;
  • One or more applications wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to:
  • a certain amount of information is selected as candidate information in descending order of matching degree and recommended to the user in order of matching degree from large to small.
  • the terminal device includes a processor 310, a memory 320, an internal memory 330, a network interface 340, and a display screen 350 connected through a system bus.
  • the processor 310 is configured to implement a computing function and a function of controlling the operation of the terminal device, and the processor 310 is configured to perform the method for recommending application information based on the installed application provided by the above embodiment.
  • the processor 310 is configured to determine the similarity between the installed information of the application and the different information in the information flow library; use the obtained similarity to obtain the matching degree of the user to different information; and select a certain order according to the matching degree from large to small The amount of information is used as candidate information and is recommended to the user in descending order of matching.
  • the memory 320 is a non-volatile storage medium storing an operating system 321, a database 322, and a computer program for implementing the method for recommending application information based on the installed application provided by the above embodiment, and a candidate for executing the computer program generation Intermediate data and result data.
  • Network interface 340 is used to communicate with the server, and network interface 340 includes a radio frequency transceiver.
  • the present application also provides a computer readable storage medium carrying one or more computer instruction programs thereon, the one or more processors executing one or more processors executing one
  • the method for recommending application information based on the installed application includes: determining the similarity between the installed information of the user and the different information in the information flow library; using the obtained similarity degree to obtain the matching degree of the user to different information; A certain amount of information is selected as candidate information in descending order and recommended to the user in descending order of matching.
  • the foregoing program may be stored in a computer readable storage medium, and the program is executed when executed.
  • the foregoing storage medium includes: a mobile storage device, a random access memory (RAM), a read-only memory (ROM), a magnetic disk, or an optical disk.
  • RAM random access memory
  • ROM read-only memory
  • magnetic disk or an optical disk.
  • optical disk A medium that can store program code.
  • the above-described integrated unit of the present application may be stored in a computer readable storage medium if it is implemented in the form of a software function module and sold or used as a stand-alone product.
  • the technical solution of the embodiments of the present application may be embodied in the form of a software product in essence or in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for making
  • a computer device which may be a personal computer, server, or network device, etc.
  • the foregoing storage medium includes various media that can store program codes, such as a mobile storage device, a RAM, a ROM, a magnetic disk, or an optical disk.
  • the method and apparatus for recommending application information based on an installed application according to the present application fully considers the interests and hobbies of the user, and determines the similarity of the different information in the installed application and the information flow library, and then determines the user's different information.
  • Matching degree thereby selecting a certain number of candidate information, may recommend corresponding candidate information to the user according to the matching degree from large to small, or may select appropriate information from the candidate information according to the information quality indicator to recommend to the user, which can Different application information recommended according to different users' interests and hobbies, thereby achieving personalized recommendation, improving the viewing rate of application information and the usage rate of the application.

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Abstract

L'invention concerne un procédé et un dispositif permettant de recommander des informations d'application sur la base d'une application installée. Le procédé consiste à : déterminer la similarité entre une application installée par un utilisateur et différentes informations dans une bibliothèque de flux d'informations ; obtenir les degrés de correspondance entre l'utilisateur et les différentes informations à l'aide de la similarité obtenue ; et sélectionner un certain volume d'informations en tant qu'informations candidates en fonction d'un ordre décroissant de degrés de correspondance, puis recommander les informations candidates à l'utilisateur en fonction de l'ordre décroissant des degrés de correspondance.
PCT/CN2017/119610 2016-12-29 2017-12-28 Procédé et dispositif de recommandation d'informations d'application sur la base d'une application installée, dispositif terminal et support de stockage WO2018121700A1 (fr)

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CN201611242501.6 2016-12-29
CN201611242501.6A CN106846094A (zh) 2016-12-29 2016-12-29 一种基于已安装应用来推荐应用信息的方法和装置

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CN106846094A (zh) * 2016-12-29 2017-06-13 广州优视网络科技有限公司 一种基于已安装应用来推荐应用信息的方法和装置
CN106897412A (zh) * 2017-02-20 2017-06-27 广州优视网络科技有限公司 一种基于目标应用推荐相关联应用的方法和装置
CN110020152B (zh) * 2017-12-04 2021-03-19 中国移动通信集团终端有限公司 应用推荐方法及装置
CN108052671B (zh) * 2017-12-29 2021-01-08 北京奇元科技有限公司 一种基于动态匹配进行内容项推送的方法及系统
CN108510352A (zh) * 2018-02-09 2018-09-07 广州优视网络科技有限公司 应用专题推荐方法、装置及计算机设备
CN108769125B (zh) * 2018-04-28 2021-08-17 阿里巴巴（中国）有限公司 应用推荐方法、装置、存储介质及计算机设备
CN108809987B (zh) * 2018-06-14 2020-10-23 江苏果米文化发展有限公司 一种基于大数据分析的网游推广方法
CN109242643B (zh) * 2018-09-30 2021-05-28 武汉斗鱼网络科技有限公司 应用于直播平台的游戏推荐的方法、装置以及存储介质
CN111796883B (zh) * 2019-04-09 2024-05-14 Oppo广东移动通信有限公司 设备控制方法、装置、存储介质及电子设备
CN110134410B (zh) * 2019-05-17 2022-11-15 北京百度网讯科技有限公司 冷启动用户的个性化信息关联方法、装置及存储介质
CN111611143B (zh) * 2020-05-20 2023-06-30 每日互动股份有限公司 数据处理方法及装置、电子设备和计算机可读存储介质
CN112328935B (zh) * 2020-10-29 2021-11-12 朱理薇 基于大数据的应用程序推送系统及方法
CN115150346A (zh) * 2022-07-04 2022-10-04 中国银行股份有限公司 信息推送方法及装置

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103679502A (zh) * 2013-11-14 2014-03-26 青岛海信传媒网络技术有限公司 应用推荐方法和系统
CN105912550A (zh) * 2015-12-15 2016-08-31 乐视网信息技术（北京）股份有限公司 一种移动终端的信息推荐方法及装置
CN105989074A (zh) * 2015-02-09 2016-10-05 北京字节跳动科技有限公司 一种通过移动设备信息进行推荐冷启动的方法和装置
CN106846094A (zh) * 2016-12-29 2017-06-13 广州优视网络科技有限公司 一种基于已安装应用来推荐应用信息的方法和装置

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105989108A (zh) * 2015-02-12 2016-10-05 广东欧珀移动通信有限公司 一种信息推荐方法及装置
CN105426514B (zh) * 2015-11-30 2019-03-01 扬州大学 个性化的移动应用app推荐方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103679502A (zh) * 2013-11-14 2014-03-26 青岛海信传媒网络技术有限公司 应用推荐方法和系统
CN105989074A (zh) * 2015-02-09 2016-10-05 北京字节跳动科技有限公司 一种通过移动设备信息进行推荐冷启动的方法和装置
CN105912550A (zh) * 2015-12-15 2016-08-31 乐视网信息技术（北京）股份有限公司 一种移动终端的信息推荐方法及装置
CN106846094A (zh) * 2016-12-29 2017-06-13 广州优视网络科技有限公司 一种基于已安装应用来推荐应用信息的方法和装置

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