WO2001037162A2 - Procede et systeme de recommandation sur la base de l'interet - Google Patents

Procede et systeme de recommandation sur la base de l'interet Download PDF

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Publication number
WO2001037162A2
WO2001037162A2 PCT/US2000/028005 US0028005W WO0137162A2 WO 2001037162 A2 WO2001037162 A2 WO 2001037162A2 US 0028005 W US0028005 W US 0028005W WO 0137162 A2 WO0137162 A2 WO 0137162A2
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Prior art keywords
user
data
affinity
interest
recommendation
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PCT/US2000/028005
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English (en)
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WO2001037162A8 (fr
Inventor
Paul Bieganski
Robert W. Drillskill, Jr.
Daniel S. Frankowski
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Net Perceptions, Inc.
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Priority to AU11954/01A priority Critical patent/AU1195401A/en
Publication of WO2001037162A2 publication Critical patent/WO2001037162A2/fr
Publication of WO2001037162A8 publication Critical patent/WO2001037162A8/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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • This invention relates generally to data processing systems, and more particularly, to recommendation systems.
  • Recommendation systems are becoming widely used in e-commerce business activities. Recommendation systems allow e-commerce operators to take advantage of customer databases to provide valuable personalized service to customers. For example, systems that make personalized recommendations are used as a marketing tool to turn "window shoppers" into buyers, increase cross-sells and up-sells, and deepen customer loyalty.
  • Unary data is a set of user-item pairs that indicate an event of interest to the user has occurred.
  • An example of unary data is purchase record data where a user-item pair indicates that the user has purchased a particular item.
  • Likert scale data indicates a user's preferences about an item. Typically, likert scales give the user options, such as: like very much "5"; like a little "4"; don't mind either way “3"; dislike a little “2”; and strongly dislike "1.”
  • Likert data based calculations use a form of correlation calculation that assumes negative information within the data. This negative data can be used to find users with similar or opposite interests.
  • Likert data based calculations do not account for data that is only positive. Instead, these calculations handle low positive data (e.g., a user rating an item a "1") as a negative connotation such as "strongly dislike.” Therefore, although existing recommendation systems provide recommendations based on unary data and likert scale data, these systems do not provide recommendations when the data contains only positive interest data. There exists a need to improve existing recommendation systems to provide recommendations based on other types of data, such as interest data.
  • Methods and systems consistent with the present invention provide a recommendation server capable of using interest data to provide a recommendation to a user.
  • Interest data is a type of data that represents a measure of the level of interest someone has expressed in an entity.
  • methods and systems consistent with the present invention locate potential neighbors that have rated entities similar to those rated by the user. Once these neighbors are located, an affinity value is calculated between the user and potential neighbor to determine whether the potential neighbor's ratings are closely related to that of the user's ratings. If a user and a neighbor have an affinity greater than a predetermined threshold, that neighbor is considered close enough to the user to provide a recommendation for various entities.
  • a method provides a recommendation using resource allocation data.
  • the method obtains resource allocation data corresponding to a first user, determines an affinity between the first user and one of the other users based on the resource allocation data, and provides a recommendation to the user based on the affinity.
  • a method indicates strength of an interest of a user in a particular entity.
  • 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 data, determines an affinity between the user and one of the plurality of neighbors based on the resource allocation data, includes the one of the located neighbors meeting predetermined criteria on a neighbor list, and provides a recommendation to the user based on the neighbor list.
  • a method provides a user with an electronic coupon based on purchase data.
  • the method obtains purchase data corresponding to the user, generates interest data from the purchase data, determines an affinity between the first user and a neighbor based on the generated interest data, and provides the user with an electronic coupon based on the affinity.
  • a method provides a recommendation using resource allocation data that indicates a user's strength of an interest in a particular entity.
  • 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 as the user, determines an affinity between the user and one of the other users based on the similar strength of an interest and provides a recommendation to the user based on a list that contains a set of other users meeting predetermined criteria.
  • Figure 1 depicts a data processing system suitable for practicing methods and systems consistent with the present invention
  • Figure 2 depicts a more detailed diagram of the client computer depicted in Fig. 1 ;
  • 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 providing a recommendation with interest data
  • Figure 5A depicts a first rating table for use with methods and systems consistent with the present invention
  • Figure 5B depicts a first normalized rating table for the first rating table of Fig. 5A
  • Figure 5C depicts a second rating table for use with methods and systems consistent with the present invention
  • Figure 5D depicts a second normalized rating table for the first rating table of Fig. 5C.
  • Figure 6 depicts an embodiment of an electronic commerce server for use with the invention.
  • Methods and systems consistent with the present invention provide a recommendation server capable of using interest data to provide a recommendation to a user.
  • Interest data is a type of data that represents a measure of the level of interest someone has expressed in an entity.
  • methods and systems consistent with the present invention locate potential neighbors that have rated entities similar to those rated by the user. Once these neighbors are located, an affinity value is calculated between the user and potential neighbor to determine whether the potential neighbor's ratings are closely related to that of the user's ratings. If a user and a neighbor have an affinity greater than a predetermined threshold, that neighbor is considered close enough to the user to provide a recommendation for various entities.
  • a recommendation may be used as part of marketing campaigns that recommend entities to users who are interested in similar entities; as part of knowledge-management systems in large corporations that recommends reports and documents to employees based on the employees business or research interests; as part of call centers that provide recommendations for merchandise to consumers placing orders; and as part of an electronic coupon campaign that provides electronic coupons to users visiting various web sites.
  • Fig. 1 depicts a data processing system 100 suitable for practicing methods and systems consistent with the present invention.
  • Data processing system 100 comprises a client computer 112 connected to recommendation server 120 via a network 130, such as the Internet.
  • a user uses client computer 112 to provide various information to recommendation server 120.
  • client computer 1120 may contain many more client computers and additional client sites.
  • recommendation server 120 may be located at various places on network 130, including client computer 112.
  • Figure 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 users to interact with recommendation server 120 by transmitting and receiving files, such as web pages.
  • a web page may include images or instructions to obtain recommendation requests from a user using hypertext markup language (HTML), Java or other techniques.
  • HTML hypertext markup language
  • Java Java
  • An example of a browser suitable for use with methods and systems consistent with the present invention is the Netscape Navigator browser, from Netscape.
  • 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 interest gatherer engine 314.
  • Recommendation engine 312 determines if an item should be recommended to the user. It may use many different techniques to generate recommendations based on user interest profiles.
  • One technique that may be used to generate recommendations is automated collaborative filtering as described in Resnick, lacovo, Susha, Bergstrom, and Riedl, "GroupLens: An Open Architecture For Collaborative Filtering Of Netnews," Proceedings of the 1994 Computer Supported Collaborative Work Conference (1994).
  • Other recommendation techniques are described in U.S. application serial no.
  • Recommender systems may also be based on well-known CF systems, logical rules derived from data, or on statistical or machine learning technology.
  • a recommender system may use well-known rule-induction learning, such as Cohen's Ripper, to learn a set of rules from a collection of data as described in Good, N., Schafer, J.B., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., and Riedl, J., "Combining Collaborative Filtering with Personal Agents for Better Recommendations," Proceedings of the 1999 Conference of the American Association of Artifical Intelligence (AAAI-99).
  • Recommender systems may also be based on well-known data mining techniques that include a variety of supervised and unsupervised learning strategies and produce "surprising" results expressed as associations or rules embedded in a data set.
  • Recommender systems may also contain rating functions (models) programmed by a system administrator.
  • the rating functions are either a formula or a table of ratings that determines business goals (e.g., the formula may specify a low rating for low-stock and out-of-stock items).
  • These mentioned systems also require user data as input to produce personalized recommendations for users.
  • recommendation engine 312 may use a web page, Application Program Interface (API), or other input interface.
  • API Application Program Interface
  • An API is a set of routines, protocols, or tools for communicating with software applications. APIs provide efficient access to the recommendation engine without the need for additional software to interface with the recommendation engine. Ratings may come in various forms. For example, a rating may be a user's interest in a particular entity, called interest data.
  • Interest data is a type of data that represents a measure of the level of interest a user has expressed an entity. Interest data is always a positive measure (e.g., it is assumed that a user cannot show an interest in an entity they dislike). Interest data may also be resource allocation data. Resource allocation data is a type of data where the user indicates, not only an entity of interest, but also how much interest the user has in the entities. For example, if a user has $1000 to spend on mutual funds, he may allocate his resource (money) to have $250 in mutual fund A, $750 in mutual fund B, and 0 in mutual fund C. The user has a higher interest in mutual fund B than in mutual fund A, and no interest in mutual fund C.
  • Interest data may also be based on user purchase data. That is, the interest data would include a list of entities recently purchased by the user. A user that purchases more of entity A that entity B would have a higher interest in A than B. For example, if a user recently purchases entity A and entity B, and afterwards purchases ten more of entity A, the user has a higher interest in entity A than B.
  • Interest gatherer engine 314 collects interest data from various users and stores the collected interest data in interest table 324.
  • Secondary storage device 320 includes a database 322 that stores various user's interests in interest data table 324.
  • Interest data table 324 obtains interest data by receiving ratings from users from places such as, web page logs, previous purchases or any application that can obtain user preferences.
  • a web page log is a written record of all activity on a particular web site. For example, a web page log may contain a user's time on a web page, and web pages viewed.
  • database 322 may contain other types of data, such as unary data and likert data.
  • Figure 4 depicts a flow chart of the steps performed when generating a recommendation with interest data.
  • the first step is to receive a request for a recommendation from a user (step 402).
  • the request may come in many forms.
  • a recommendation request may come from an e-commerce server that will display a list of entities to a user before "check-out.”
  • the request may also come from a particular web page viewed by a user, or by monitoring "click-stream" data.
  • 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 server may query recommendation engine with a "predict" API at the time the user displays finalizes his shopping cart.
  • a request for a recommendation may also come form an entity, or a group of entities (e.g., entities that are within the same category).
  • recommendation engine 312 may recommend to the entity a list of users that may be interested in that entity.
  • recommendation engine 312 may extract interest data pertaining to the user from interest data table 324 (step 404). If no interest data is available for the user, recommendation engine 312 may provide a default list. A default list would contain a preprogrammed list of entities to recommend to the user. For example, if the user has never used recommendation engine 312 before, it may provide a top ten list of best selling entities to the user.
  • Recommendation engine 312 uses the extracted interest data to locate potential neighbors (step 406).
  • the term "neighbor" means users identified in interest data table 324 with similar interests as the first user. For example, if another user in interest data table 324 has rated similar entities as the first user, the other user may be considered a potential neighbor. At this point, the user is considered a potential neighbor since the affinity between the user and the entity would still need to be determined, as further described below. For example, an ideal neighbor for a user would be a neighbor that has rated all entities that the user has also rated. If no potential neighbors are found (step 408), recommendation engine 312 attempts to locate any neighbors to provide a recommendation (step 410).
  • recommendation engine 312 uses a default list instead of providing a recommendation, as described above (step 420). If, however, recommendation engine 312 locates neighbors (step 410), recommendation engine 312 uses the located neighbors to provide a recommendation (step 422).
  • recommendation engine 312 computes an affinity between the user and the potential neighbor using an appropriate affinity equation (step 412).
  • affinity equations provide an affinity value that indicates how much in common the two users are and how similar their preferences are.
  • the mutual normalized interest equation is one affinity equation that uses normalized interest information, such as normalized ratings, to return a common interest level between the user and the potential neighbor. To do so, the equation computes the sum of the minimum normalized coratings.
  • a corating is a pair of ratings for the users. For example, Fig 5A depicts a rating table 500 containing common ratings between a user and a potential neighbor. Fig. 5B depicts a normalized table 510 containing normalized data from rating table 500.
  • the value ".6" is an affinity value between the user and the potential neighbor. Fuzzy Evidence Set Similarity
  • the fuzzy evidence set similarity equation is another affinity equation that uses normalized interest information, such as normalized ratings, to return the amount of interest overlap between the user and the potential neighbor. More information regarding fuzzy evidence may be found in Zimmerman, H.J., "Fuzzy Set Theory - And Its Applications," Second Revised Edition, 1991 , hereby incorporated by reference.
  • Fig. 5C depicts a rating table 520 containing some common ratings between a user and a potential neighbor.
  • Fig. 5D depicts a normalized table 530 containing normalized data from rating table 520.
  • fuzzy evidence set similarity is computed using the following equation, where "noncoratingj” is the number of entities “R” has not rated, “noncorating_k” is the number of entities “r” has not rated, and “coratingsj” is the number of entities both users have not rated:
  • recommendation engine 312 determines if the affinity value is above a predetermined threshold value (step 414).
  • a predetermined 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 neighbor list (step 416). 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 interest data table 324 (step 406).
  • Recommendation engine 312 locates neighbors until enough neighbors have been located (step 418). 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 requisite number of neighbors has been located, recommendation engine 312 may provide a recommendation to the user using well-known recommendation techniques (step 424).
  • FIG. 6 illustrates a recommendation system integrated into a web-based electronic mutual fund site (e-commerce site).
  • the user at computer 602 connects using a network 604 to a web server 606.
  • Web server 606 presents this set of products for sale to the user.
  • a recommendation server 610 coupled to the web server 606 and commerce server 608 receives purchase information from commerce server 608.
  • the recommendation server 610 uses web server 606 and commerce server 608 to provide the user with specifically targeted content, such as recommendations to purchase specific entities, recommendations to view specific entities, or targeted advertisement.
  • Recommendation server 610 does so by maintaining records of previous purchases and quantity of purchases by the user and other users.
  • a user may purchase $1000 of mutual fund A, $500 of mutual fund B, and $2000 of mutual fund C.
  • the commerce server records the purchase and provides the recommendation server with the data.
  • the recommendation server may then compare user 602 portfolio to other user's portfolios maintained in the recommendation server using an interest affinity equation.
  • the users that have high affinities with user 602 are considered neighbors and are included on a neighbor list that is used to provide recommendations to user 602. For example, if another user has $1000 mutual fund A, $1000 mutual fund B, and $1000 in mutual fund D, recommendation server 610 may recommend that user 602 consider mutual fund D as a potential investment.
  • Methods and systems consistent with the present invention provide a recommendation server capable of using interest data to provide a recommendation to a user.
  • the recommendation server contains software to provide interest data recommendations to the user.
  • the software may provide recommendations of users to an item, or groups of items.
  • the recommendation server applies an affinity equation to the set of interest data.

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Abstract

L'invention concerne des procédés et des systèmes utilisant un serveur de recommandation capable d'utiliser des données d'intérêt pour faire des recommandations à un utilisateur. Les données d'intérêt sont des données qui représentent une mesure du niveau de l'intérêt exprimé par une personne sur une entité. L'invention concerne en particulier des procédés et des systèmes permettant de localiser des voisins potentiels qui ont évalué des entités similaires à celles évaluées par l'utilisateur. Une fois que ces voisins ont été localisés, une valeur d'affinité est calculée entre l'utilisateur et le voisin potentiel afin de déterminer si les évaluations du voisin potentiel sont étroitement liées à celles de l'utilisateur. Si un utilisateur et un voisin présentent une valeur d'affinité supérieure à une valeur seuil prédéterminée, ce voisin est considéré suffisamment proche de l'utilisateur pour pouvoir lui faire des recommandations sur plusieurs entités.
PCT/US2000/028005 1999-11-12 2000-10-11 Procede et systeme de recommandation sur la base de l'interet WO2001037162A2 (fr)

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AU11954/01A AU1195401A (en) 1999-11-12 2000-10-11 Interest based recommendation method and system

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US43866499A 1999-11-12 1999-11-12
US09/438,664 1999-11-12

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WO2001037162A8 (fr) 2002-06-13

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