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- W2030484290 abstract "The increasing amount of side information associated with the items in E-commerce applications has provided a very rich source of information that, once properly exploited and incorporated, can significantly improve the performance of the conventional recommender systems. This paper focuses on developing effective algorithms that utilize item side information for top-N recommender systems. A set of sparse linear methods with side information (SSLIM) is proposed, which involve a regularized optimization process to learn a sparse aggregation coefficient matrix based on both user-item purchase profiles and item side information. This aggregation coefficient matrix is used within an item-based recommendation framework to generate recommendations for the users. Our experimental results demonstrate that SSLIM outperforms other methods in effectively utilizing side information and achieving performance improvement." @default.
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- W2030484290 date "2012-09-09" @default.
- W2030484290 modified "2023-10-14" @default.
- W2030484290 title "Sparse linear methods with side information for top-n recommendations" @default.
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- W2030484290 doi "https://doi.org/10.1145/2365952.2365983" @default.
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