Leveraging aggregate ratings for improving predictive performance of recommender systems

Authors:
Akhmed Umyarov
Affiliations:
New York University, New York, NY, USA
Venue:
Proceedings of the 2008 ACM conference on Recommender systems
Year:
2008

Citing 3
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Item-based collaborative filtering recommendation algorithms

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Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

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Leveraging aggregate ratings for better recommendations

Proceedings of the 2007 ACM conference on Recommender systems

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Abstract

One of the key problems in recommender systems is accurate estimation of unknown ratings of individual items for individual users in terms of the previously specified ratings and other characteristics of items and users. In this thesis, we investigate a way of improving estimations of individual ratings using externally provided properties of aggregate ratings for groups of items and users, such as an externally specified average rating of action movies provided by graduate students or externally specified standard deviation of ratings for comedy movies.