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| Most of implicit feedback based recommendation algorithms only utilize little auxiliary feedback, which leads to the inaccuracy recommendation in data sparsity. However, several user actions on e-commerce, such as click, wanted, purchased, can provide extra potential and valuable information for recommender systems. To make full use of these actions, this paper proposes a tensor-based recommendation algorithm using heterogeneous implicit feedback. This scheme exposes the hidden dependency among users, items and actions and breaks the limitation of user-item matrix. Moreover, it also considers the social information as regularization term to obtain trust relationship between users and their friends. The experimental results on a real dataset show our proposed algorithm outperforms other compared methods, and improving the performance of recommender system effectively |
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| Keywords:recommender system; hetergeneous implicit feedback; tensor-based; social regularizaton; data sparsity |
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