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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Kim, HN., Ji, AT., Yeon, C., Jo, GS. (2007). A User-Item Predictive Model for Collaborative Filtering Recommendation. In: Conati, C., McCoy, K., Paliouras, G. (eds) User Modeling 2007. UM 2007. Lecture Notes in Computer Science(), vol 4511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73078-1_38
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DOI: https://doi.org/10.1007/978-3-540-73078-1_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73077-4
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