Recommendation system capable of adapting to user feedback

Abstract

A recommendation system uses feedback from users on specific item recommendations to assess the quality of the recommendation rules used to generate such recommendations. The feedback may be explicit (e.g., a user rates a particular recommended item), implicit (e.g., a user purchases a recommended item), or both. The system may use these assessments to modify the degree to which particular recommendation rules are used to generate recommendations. For instance, if a particular recommendation rule leads to negative feedback relatively frequently, the system reduce or terminate its reliance on the rule. In some embodiments, the system may also increase its reliance on recommendation rules that tend to produce positive feedback.

Description

PRIORITY CLAIM[0001]This application is a continuation of U.S. application Ser. No. 11 / 769,586, filed Jun. 27, 2007, the disclosure of which is hereby incorporated by reference.TECHNICAL FIELD[0002]The present disclosure relates to recommendation systems, including recommendation systems that use recommendation rules to select items to recommend to users.BACKGROUND[0003]Some web sites and other types of interactive systems implement recommendation services for generating personalized recommendations of items stored or represented in a data repository. One common application for recommendation services involves recommending products for purchase, rental, subscription, viewing or some other form of consumption. For example, some e-commerce web sites provide services for recommending products to users based on their respective purchase histories, rental histories, product viewing histories, or item ratings. Recommendation services are also used to recommend web sites, news articles, us...

AI Technical Summary

Problems solved by technology

One problem with existing recommendation systems of the type described above is that they are susceptible to generating poor recommendations as the result of “low-quality” recommendation rules.

Such low-quality recommendation rules may, for example, result from aberrational user activity over a period of time, or from limitations in the mining algorithms used to generate the recommendation rules.

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