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• 311 creator bin-xu.
• 311 creator chun-chen.
• 311 creator deng-cai.
• 311 creator jiajun-bu.
• 311 type InProceedings.
• 311 label "An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups".
• 311 sameAs 311.
• 311 abstract "Collaborative filtering (CF) is one of the most successful recommendation approaches. CF-based recommender systems typically associate a user with a group of like-minded users based on their preferences over all the items, and then provide items enjoyed by others in the group. However we find that two users with similar tastes on one item subset may have totally different tastes on another set. That is to say, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more natural to make preference predictions for a user via the correlated subgroups than the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it. Then we propose an unified framework to combine subgroups with (any) pure CF algorithms for improving their top-N recommendation performance. Our approach can be seen as an extension of traditional clustering CF models. Systematic experiments on three real world data sets show the effectiveness of our proposed approach.".
• 311 hasAuthorList authorList.
• 311 isPartOf proceedings.
• 311 keyword "Clustering Model".
• 311 keyword "Collaborative Filtering".
• 311 keyword "Recommender Systems".
• 311 keyword "User-Item Subgroups".
• 311 title "An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups".