论文信息 - Improve general contextual slim recommendation algorithms by factorizing contexts

Improve general contextual slim recommendation algorithms by factorizing contexts

Context-aware recommender systems (CARS) emerged during recent years in order to adapt to users' preferences in different contextual situations. For example, users may choose different movies if they are going to see movies with their partners rather than with kids. The motivation behind is that users' preferences on items are always changing from contexts to contexts.

Yong Zheng

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