@inproceedings{4ee963f44f92446482e2103720cc1ad0,
title = "A user-item predictive model for collaborative filtering recommendation",
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.",
author = "Kim, {Heung Nam} and Ji, {Ae Ttie} and Cheol Yeon and Jo, {Geun Sik}",
year = "2007",
doi = "10.1007/978-3-540-73078-1_38",
language = "English",
isbn = "9783540730774",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "324--328",
booktitle = "User Modeling 2007 - 11th International Conference, UM 2007, Proceedings",
address = "Germany",
note = "11th International on User Modeling Conference, UM 2007 ; Conference date: 25-06-2007 Through 29-06-2007",
}