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- W2901826172 abstract "It is known that a high quality recommendation is important for recommender systems to provide users who are faced with overwhelming media content and information with more personalized recommendation of items. But, existing recommending methods still have some problems, such as those based on collaborative filtering methods suffer from so-called cold-start problem and data sparsity problem because of excessive reliance on rating mechanism. To solve these two problems, UNCACR a novel context-aware recommendation method based on user neighbor clustering is proposed in this paper. First of all, the historical ratings and context similarities between users are taken into consideration to form user graph. Secondly, a partitioning-based algorithm is applied on the graph to find the initial center sets. Then, the neighbor clustering algorithm is performed to calculate the new cluster center set and to form user neighbor set. Finally, the neighbor set for a new user can be found and top_N personalized items are predicted for active users. The experimental results on the two real-world datasets show the advantages of proposed method which outperforms several existing recommend methods." @default.
- W2901826172 created "2018-11-29" @default.
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- W2901826172 date "2018-08-01" @default.
- W2901826172 modified "2023-09-24" @default.
- W2901826172 title "User Neighbor Clustering Based Context-Aware Recommendation Method" @default.
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- W2901826172 doi "https://doi.org/10.1109/ihmsc.2018.00023" @default.
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