[Publication] A User-Item Predictive Model for Collaborative Filtering Recommendation
2007/06/22 14:44
Heung-Nam Kim, Ae-Ttie Ji, Cheol Yeon, and Geun-Sik Jo,
"A User-Item Predictive Model for Collaborative Filtering Recommendation",
Lecture Notes in Artificial Intelligence (11th International Conference on User Modeling),
Vol. 4511, pp. 334-338, Springer-Verlag, Jun. 2007
"A User-Item Predictive Model for Collaborative Filtering Recommendation",
Lecture Notes in Artificial Intelligence (11th International Conference on User Modeling),
Vol. 4511, pp. 334-338, Springer-Verlag, Jun. 2007
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.
