Method and apparatus for an adaptive stereotypical profile for recommending items representing a user's interests

• US 20030233655A1
• Filed: 06/18/2002
• Published: 12/18/2003
• Est. Priority Date: 06/18/2002
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Gutta, Srinivas, Kurapati, Kaushal
• Application Number:
  US10/174,450
• Publication Number:
  US 20030233655A1
• Time in Patent Office: Days
• US Class Current: 725/46
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/475 End-user interface for inputting end-user data, e.g. personal identification number [PIN], preference data
• CPC Class Codes: H04N 21/4755 for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

A method and apparatus are disclosed for recommending items of interest to a user, such as television program recommendations. According to the principals of the invention, initial recommendations, which may be generated before a viewing history or purchase history of the user is available, are adapted or transformed to better capture a users viewing behavior using a feedback process. In particular, stereotypes are generated, which are used to build a stereotypical profiles. Stereotypical profiles are then generated that reflect the typical patterns of items selected by representative viewers. Recommendations are computed against a ground truth data using the stereotypical profiles, wherein distances are computed between each show in a so called ground truth data with the centroid of each stereotype in the stereotypical profile. If there is disagreement between what is computed recommendation and the original ground truth data, then additional feedback is solicited from a user, which is used to create a meta-profile. A meta-profile consists of the set of all weights the user has provided for the shows that he/she wants the shows to be recommended or discarded (e.g. positive/negative reinforcement). Lastly, the recommendation is recomputed using the meta-profile against the stereotypical profile.

Claims

1. A method for use in a recommender for recommending items of interest to a user, said method comprising the steps of:

generating an initial recommendations using stereotypical profiles and a ground truth data;

obtaining user feedback regarding the recommendation if the initial recommendation and ground truth data disagree; and

generating a revised recommendation using the user feedback.

2. The method of claim 1, wherein said generating an initial recommendations step includes generating stereotypes, which are used to build the stereotypical profiles.

3. The method of claim 2, wherein said generating an initial recommendations step includes computing a distance between each item in the ground truth data with the centroid of each stereotype in the stereotypical profile.

4. The method of claim 3, wherein said generating a revised recommendation includes creating a meta-profile, said meta-profile includes a set of weight factors based on the user feedback, and said meta-profile is used to generate the revised recommendation.

5. The method of claim 3, wherein said distance, D, between two values, S₁ and S₂ for a specific symbolic feature is given by:

6. The method of claim 4, wherein said generating a revised recommendation includes computing a revised distance, D, by applying the meta-profile, W, against the stereotypical profile and is given by:

7. The method of claim 1, wherein said items are programs.

8. The method of claim 1, wherein said items are content.

9. The method of claim 1, wherein said items are products.

10. A system for use in a recommender for recommending items of interest to a user, comprising:

a memory for storing computer readable code; and

a processor operatively coupled to said memory, said processor configured to;

generate an initial recommendation using stereotypical profiles and a ground truth data;

solicit user feedback regarding the recommendation if the initial recommendation and ground truth data disagree; and

generate a revised recommendation using the user feedback.

11. A system for use in a recommender for recommending items of interest to a user, comprising:

means for generating an initial recommendations using stereotypical profiles and a ground truth data;

means for soliciting user feedback regarding the recommendation if the initial recommendation and ground truth data disagree; and

means for generating a revised recommendation using the user feedback.

12. An article of manufacture for use with a recommender for recommending items of interest to a user, comprising:

a computer readable medium having computer readable code means embodied thereon, said computer readable program code means comprising;

a step to generate an initial recommendation using stereotypical profiles and a ground truth data;

a step to obtain user feedback regarding the recommendation if the initial recommendation and ground truth data disagree; and

a step to generate a revised recommendation using the user feedback.