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WO2001053973A2 - Procede et systeme de recommandations se basant sur des donnees cloisonnees d'espace de notation - Google Patents

Procede et systeme de recommandations se basant sur des donnees cloisonnees d'espace de notation Download PDF

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Publication number
WO2001053973A2
WO2001053973A2 PCT/US2001/001643 US0101643W WO0153973A2 WO 2001053973 A2 WO2001053973 A2 WO 2001053973A2 US 0101643 W US0101643 W US 0101643W WO 0153973 A2 WO0153973 A2 WO 0153973A2
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WO
WIPO (PCT)
Prior art keywords
user
data
users
affinity
recommendation
Prior art date
Application number
PCT/US2001/001643
Other languages
English (en)
Other versions
WO2001053973A3 (fr
Inventor
Daniel Frankowski
Paul Bieganski
Robert Driskill
Valerie Guralnik
Filip Mulier
Original Assignee
Net Perceptions, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Net Perceptions, Inc. filed Critical Net Perceptions, Inc.
Priority to AU2001232846A priority Critical patent/AU2001232846A1/en
Publication of WO2001053973A2 publication Critical patent/WO2001053973A2/fr
Publication of WO2001053973A3 publication Critical patent/WO2001053973A3/fr

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Classifications

    • 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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/951—Indexing; Web crawling techniques

Definitions

  • This invention relates generally to data processing systems, and more particularly,
  • Unary data is a set of user-item pairs that
  • purchase record data where a user-item pair indicates that the user has purchased a
  • Likert scale data indicates that the event has occurred.
  • Likert scale data indicates a user's preferences about an item. Typically, Likert scales give the user options, such as: like very much "5";
  • Likert data based calculations use a form of correlation calculations that assumes negative information and positive information within the same data. Thus, calculations using Likert data must accept, and cannot differentiate or separate, positive and negative data. For example, these calculations handle low positive data (e.g., a user rating an item a " 1 ")
  • Affinity values are calculated from a series of
  • the method analyzes partitioned preference data that reflects
  • the method also provides a recommendation based on the determined
  • the method obtains click-stream data corresponding to the user, locates a plurality of neighbors with click-stream data similar to the user's click-stream
  • the method includes the one of
  • the method locates, in a database that contains resource allocation data for a plurality of users, other users with a similar strength of an interest
  • the method determines an affinity between the user and one of the other users based on the similar strength of an interest.
  • the method then provides a recommendation to the user based on a list that contains a set of other users meeting predetermined criteria.
  • the method locates
  • the method also includes the one of the located neighbors meeting
  • the affinity is
  • the method also provides a recommendation to the
  • a method generates a recommendation for
  • the method locates at least one potential neighbor for a user from a pool of candidates using a search strategy, and determines an affinity between the user and a
  • a computer-implemented recommendation method is disclosed. The method permits a user to submit a request for a
  • recommendation provides user ratings corresponding to each one of a set of users with
  • the method also provides a recommendation based on the determined affinity.
  • Figure 1 depicts a data processing system suitable for practicing methods
  • FIG. 2 depicts a more detailed diagram of the client computer depicted in
  • Figure 3 depicts a more detailed diagram of the recommendation server depicted in Fig. 1 ;
  • Figure 4 depicts a flow chart of the steps performed when collecting data in a manner consistent with the principles of the present invention
  • Figure 5 depicts a flow chart of the steps performed when providing a recommendation consistent with the principles of the present invention
  • Figure 6A depicts a rating table for use with methods and systems in a manner consistent with the present invention
  • Figure 6B depicts an agreement table for use with methods and systems in a
  • Figure 6C depicts an interest rating table for use with methods and systems
  • Figure 6D depicts a normalized interest rating table for the interest rating table
  • Figure 6E depicts a second interest rating table for use with methods
  • Figure 6F depicts a second normalized interest rating table for the second interest rating table of Fig. 6E;
  • Figure 7A depicts an embodiment of an electronic commerce server for use
  • Figure 7B depicts another embodiment of an electronic commerce server for
  • RSP rating space partitioned
  • RSP data is a type of data that represents positive and
  • An item may be any item available
  • an item may be a book, CD, or a movie.
  • data reflects the fact a desired event has occurred, or is likely to occur. For example,
  • positive data may be a purchase event, adding an item to a shopping cart, or explicitly
  • Positive data may also indicate positive preferences for
  • Negative data reflects the fact
  • Negative data may also indicate that a desired event will not occur, or is likely not to occur.
  • RSP data may be based on implicit user
  • RSP data for that particular user/page may be negative data.
  • RSP data contains partitioned positive data and negative data
  • a search strategy may use positive data to initially narrow the number of potential neighbors, whereas the negative data may be used for minor
  • RSP data A special type of RSP data, called interest data, is a type of data that represents
  • Resource allocation data is a type of data where the user indicates, not only an item of interest, but also how much interest the user
  • Interest data may also be based on user purchase data. That is, the interest data
  • a user that purchases more of item A than item B would have a higher interest in A than B. For example, if a user
  • Resource allocation data may be considered to have multiple levels of interest (e.g., preferences), whereas purchase data is considered
  • Recommendation systems that incorporate RSP data and interest data treat
  • RSP data enables quick drill down search strategies to
  • recommendation systems provide recommendations when the data is only positive.
  • the recommendation system consistent with the
  • present invention uses a data collector to track and record any user interaction.
  • Recommendations may be used in a variety of situations. For example, a
  • recommendation may be used as part of marketing campaigns that recommend items to
  • Fig. 1 depicts a data processing system 100 suitable for practicing methods
  • Data processing system 100 comprises a
  • client computer 112 connected to recommendation server 120 via a network 130, such
  • a user uses client computer 112 to provide various information to
  • data processing system 100 may contain many more client computers and additional client sites.
  • recommendation server 120 may be located at various places on network 130. For example, the functions
  • recommendation server 120 may be included in a merchant server by client computer
  • a subset of the Internet is commonly referred to as the world wide web (www).
  • Certain computers and/or servers connected to the web (referred to as web sites) offer information in the form of web pages.
  • a web page may include digital
  • content such as text and/or images, audio streams, or instructions to obtain
  • HTTP hypertext markup language
  • FIG. 2 depicts a more detailed diagram of client computer 112, which contains a memory 220, a secondary storage device 230, a central processing unit (CPU) 240, an input device 250, and a video display 260.
  • Memory 220 includes browser 222 that allows
  • recommendation server 120 users to interact with recommendation server 120 by transmitting and receiving files, such
  • recommendation server 120 includes a memory 310, a
  • secondary storage device 320 a CPU 330, an input device 340, and a video display 350.
  • Memory 310 includes recommendation engine 312 and data collector 314.
  • Recommendation engine 312 determines if an item should be recommended to the user.
  • a recommendation system may
  • rule-induction learning such as Cohen's Ripper
  • Recommendation systems may also be based on well-known data mining
  • Recommendation systems may also contain rating functions (models) programmed by
  • the rating functions are either a formula or a table of ratings that
  • the formula may specify a low rating for low-stock and
  • Data collector 314 monitors user interaction with various applications, such as an
  • user interaction may include explicit feedback, such as survey data, or responses to recommendations, (e.g., a user
  • Data collector 314 may also monitor user interaction with various items
  • a web page log comprises a set of records of all activity on a particular web site.
  • a web page log may contain a user's time on a web page and information
  • data collector 314 identifies a user and/or statistics received by data collector 314.
  • Data collector 314 may include a web page, Application Program Interface (API),
  • API Application Program Interface
  • An API is a set of
  • APIs provide
  • Secondary storage device 320 includes a rating database 322 that stores various parameters
  • Rating database 322 obtains data by receiving parsed
  • database 322 may contain other types of data, such as unary data, and Likert.
  • Data Collection Process Figure 4 depicts a flow chart of the steps performed by data collector 314 when
  • the first step is for data collector 314 to receive user interaction data
  • data collector 314 parses various web page
  • step 404 If the received data requires processing (step 404),
  • data collector 314 processes the data (step 406). That is, data collector 314 extracts
  • data collector 314 stores the data in rating database 322 (step
  • Figure 5 depicts a flow chart of the steps performed when generating a
  • the first step is to receive a request for a
  • the request may come in many forms.
  • a recommendation request may come from an e-commerce application that will
  • Click-stream data is data obtained by monitoring users actions on particular web pages.
  • the request is submitted to recommendation engine 312 using an API.
  • the e-commerce application may query recommendation engine with a "predict" API at the
  • a request for a recommendation may also come from an item, or a group of items (e.g., items that are within the same category).
  • recommendation engine 312 may also come from an item, or a group of items (e.g., items that are within the same category).
  • recommendation engine 312 may, depending upon the type of equation, extract either positive ratings and/or
  • recommendation engine 312 may provide a default list. A default
  • list would contain a preprogrammed list of items to recommend to the user. For example, if the user has never used recommendation engine 312 before, it may provide a top ten
  • Recommendation engine 312 uses the extracted data to locate potential neighbors
  • neighbor means a user identified in rating database 322 with similar interests as the first
  • the other user may be considered a potential neighbor.
  • a candidate neighbor
  • a candidate neighbor list may be geographically constrained, or consist of an
  • a neighborhood is the list of neighbors found. Since rating database
  • 322 may contain many potential neighbors, it is desirable to first reduce the set of
  • Recommendation engine 312 may employ a search
  • Positive data is generally
  • rating database 322 contains a large number of user ratings that are generally negative, and users only rate a small portion of
  • Negative data may be later incorporated to provide recommendations using the
  • Figure 6A depicts an exemplary portion of rating database 322 containing
  • User 2 and User 3 may be potential neighbors of
  • recommendation engine 312 If no potential neighbors are found (step 508), recommendation engine 312
  • recommendation engine 312 uses a default list instead of providing
  • recommendation engine 312 locates neighbors (step 510), recommendation engine 312 uses the located neighbors
  • step 522 If, however, at least one potential neighbor is found (e.g., using a positive data
  • recommendation engine 312 computes an affinity between
  • equations consist of any combination of weighted agreement components, such as
  • a positive agreement measures a common level of positive preference between
  • the mutual normalized interest equation is a positive agreement equation that
  • FIG. 6C depicts an interest rating table 620 containing
  • Figure 6D depicts a normalized interest table 630 containing normalized data from interest rating table 620.
  • the value ".6" is an affinity value between the user and the potential neighbor.
  • the fuzzy evidence set similarity equation is another positive agreement equation that uses normalized interest information, such as normalized ratings, to return the
  • Fig. 6E depicts an interest rating table 640 containing some common ratings between a user and a potential neighbor.
  • Fig. 6F depicts a normalized
  • noncoratingj is the set of items “r” has not rated that “R” has rated
  • noncorating k is the set of items “R” has not rated that “r” has
  • coratings_i is the number of items both users have rated:
  • cosine equation is as follows:
  • a negative agreement measures a common level of negative preference between
  • the agreement computes a function using negative co-ratings of the user and the potential neighbor.
  • the negative agreement may be
  • a disagreement measures a level of disagreement in preferences between the user
  • Figure 6B depicts a completed agreement table 610 with values for various types
  • an affinity may be determined for the user
  • Equations 1-3 are special cases of equation 4, with Wp, Wn, and Wd set to different
  • present invention may provide an affinity value between User 1 and User 2 as follows:
  • one affinity equation may be more useful than another.
  • the ratings database consists of mostly positive data
  • equation 3 would be used.
  • equation 3 when the
  • ratings database consists of unreliable or sparse positive data (e.g., positive agreement on
  • recommendation engine 312 determines if the affinity value
  • step 514 is above a predetermined threshold value.
  • the threshold value may be a maximum value, minimum value, or a range of values. If the affinity value is above the threshold value, the potential neighbor is added to a
  • Each neighbor on the neighbor list provides rating information to recommendation engine 312 that is used to compute a recommendation for the user. Otherwise, if the affinity value is below the threshold value, the potential neighbor is dropped and the next potential neighbor is located in rating database 322 (step 506).
  • Recommendation engine 312 locates neighbors until enough neighbors have been
  • step 518 For example, to provide a quick recommendation, recommendation
  • engine 312 may require ten neighbors. However, to provide a more accurate
  • recommendation, recommendation engine 312 may require fifty neighbors. Once the
  • recommendation engine 312 may provide a recommendation to the user using well-known recommendation techniques (step 524).
  • FIG. 7A illustrates a recommendation system integrated into a
  • e-commerce site web-based electronic mutual fund site
  • the user at computer 702 connects using a network 704 to a web server 706.
  • web server 706 processes all financial transactions for the user and contains a database
  • Web server 706 presents this set of products for sale to
  • a recommendation server 710 coupled to the web server 706 and commerce
  • server 708 receives purchase information from commerce server 708.
  • recommendation server 710 uses web server 706 and commerce server 708 to provide the user with specifically targeted content, such as recommendations to purchase specific
  • Recommendation server 710 does so by maintaining records of previous purchases and
  • a user may purchase $1000 of mutual fund A, $500 of mutual fund B, and $2000
  • recommendation server may then compare user 702 portfolio to other user's portfolios
  • recommendation server 610 may recommend that user 602 consider mutual fund D as a potential investment.
  • the user at computer 712 connects using RSP data to provide recommendations.
  • the user at computer 712 connects using RSP data to provide recommendations.
  • E-commerce servers 716 process all
  • a recommendation server 718 coupled to network 714 receives purchase and
  • the recommendation server 718 uses
  • a user may purchase CDs A, B, and C, and return CD A, from different e-
  • server 716 records the user interaction and provides the return information as a negative
  • Recommendation server 718 may use
  • recommendation server 718 may recommend that user 712 consider to
  • Methods and systems consistent with the present invention provide a recommendation server capable of using RSP data to provide a recommendation to a user.
  • the recommendation server contains software to provide RSP data recommendations to the user.
  • the software may provide recommendations of users to an item, or groups of items. To provide the recommendations, the recommendation server applies
  • a recommendation may be generated including a list of suitable users (e.g., by using item affinities).
  • the described implementation includes software but the
  • present invention may be implemented as a combination of hardware and software or in

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  • Engineering & Computer Science (AREA)
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  • Finance (AREA)
  • Databases & Information Systems (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

L'invention porte sur des procédés et systèmes formant un système de recommandations utilisant des données positives et/ou des données négatives séparément pour localiser des voisins et fournir des recommandations à des utilisateurs. Lesdits procédés et systèmes, qui se servent des susdites données pour localiser les voisins potentiels en fonction des notations des utilisateurs, calculent les coefficients d'affinité entre un utilisateur et ses voisins potentiels, et déterminent si les notations des voisins potentiels sont très proches de celles de l'utilisateur. Si un utilisateur et l'un de ses voisins potentiels ont des affinités dépassant un seuil prédéterminé, on considère ledit voisin comme assez proche de l'utilisateur pour fournir une recommandation pour différents articles. Les coefficients d'affinité sont calculés à partir de séries d'équations d'affinité auxquelles a accès le système de recommandation.
PCT/US2001/001643 2000-01-21 2001-01-19 Procede et systeme de recommandations se basant sur des donnees cloisonnees d'espace de notation WO2001053973A2 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2001232846A AU2001232846A1 (en) 2000-01-21 2001-01-19 Recommendation method and system based on rating space partitioned data

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US17721300P 2000-01-21 2000-01-21
US60/177,213 2000-01-21
US52083700A 2000-03-08 2000-03-08
US09/520,837 2000-03-08

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WO2001053973A2 true WO2001053973A2 (fr) 2001-07-26
WO2001053973A3 WO2001053973A3 (fr) 2002-08-29

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105183748A (zh) * 2015-07-13 2015-12-23 电子科技大学 一种基于内容和评分的组合预测方法

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4996642A (en) * 1987-10-01 1991-02-26 Neonics, Inc. System and method for recommending items
US6202058B1 (en) * 1994-04-25 2001-03-13 Apple Computer, Inc. System for ranking the relevance of information objects accessed by computer users
US6041311A (en) * 1995-06-30 2000-03-21 Microsoft Corporation Method and apparatus for item recommendation using automated collaborative filtering
US5867799A (en) * 1996-04-04 1999-02-02 Lang; Andrew K. Information system and method for filtering a massive flow of information entities to meet user information classification needs
US5842199A (en) * 1996-10-18 1998-11-24 Regents Of The University Of Minnesota System, method and article of manufacture for using receiver operating curves to evaluate predictive utility
JP3116851B2 (ja) * 1997-02-24 2000-12-11 日本電気株式会社 情報フィルタリング方法及びその装置

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105183748A (zh) * 2015-07-13 2015-12-23 电子科技大学 一种基于内容和评分的组合预测方法
CN105183748B (zh) * 2015-07-13 2018-11-06 电子科技大学 一种基于内容和评分的组合预测方法

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Publication number Publication date
AU2001232846A1 (en) 2001-07-31
WO2001053973A3 (fr) 2002-08-29

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