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- W2051235496 abstract "Collaborative Filtering-based recommendation algorithms have achieved widespread success on the Web, but little work has been performed to investigate appropriate user-item relationship structures of rating matrices. This paper presents a novel and general collaborative filtering framework based on (Approximate) Bordered Block Diagonal Form structure of user-item rating matrices. We show formally that matrices in (A)BBDF structures correspond to community detection on the corresponding bipartite graphs, and they reveal relationships among users and items intuitionally in recommendation tasks. By this framework, general and special interests of a user are distinguished, which helps to improve prediction accuracy in collaborative filtering tasks. Experimental results on four real-world datasets, including the Yahoo! Music dataset, which is currently the largest, show that the proposed framework helps many traditional collaborative filtering algorithms, such as User-based, Item-based, SVD and NMF approaches, to make more accurate rating predictions. Moreover, by leveraging smaller and denser submatrices to make predictions, this framework contributes to the scalability of recommender systems." @default.
- W2051235496 created "2016-06-24" @default.
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- W2051235496 date "2013-07-28" @default.
- W2051235496 modified "2023-10-18" @default.
- W2051235496 title "Improve collaborative filtering through bordered block diagonal form matrices" @default.
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- W2051235496 doi "https://doi.org/10.1145/2484028.2484101" @default.
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