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A User-Item Predictive Model for Collaborative Filtering Recommendation I Write

Heung-Nam Kim, Ae-Ttie Ji, Cheol Yeon, and Geun-Sik Jo, "A User-Item Predictive Model for Collaborative Filtering Recommendation", Lecture Notes in Computer Science ("11th International Conference on User Modeling - UM 2007"), Vol. 4511, pp. 334-338, Springer-Verlag, Jun. 2007

+ title : A User-Item Predictive Model for Collaborative Filtering Recommendation
+ author : Heung-Nam Kim, Ae-Ttie Ji, Cheol Yeon, and Geun-Sik Jo
+ conference : 11th International Conference on User Modeling - UM 2007 (acceptance rate : 37.8%, 64/169)

abstract.
Collaborative Filtering recommender systems, one of the most representative systems for personalized recommendations in E-commerce, enable users to find the useful information easily. But traditional CF suffers from some weaknesses: scalability and real-time performance. To address these issues, we present a novel model-based CF approach to provide efficient recommendations. In addition, we propose a new method of building a model with dynamic updates, when users present explicit feedback. The experimental evaluation on MovieLens datasets shows that our method offers reasonable prediction quality as good as the best of user-based Pearson correlation coefficient algorithm.



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