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user space transformation in deep learning based recommendation

Journal of Systems Engineering and Electronics 2020年第4期31卷 674-684页

作者： WU Caihua MA Jianchao ZHANG Xiuwei XIE Dang Radar Non-Commissioned Officer School Air Force Early Warning AcademyWuhan 430345China

Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.

关键词： recommender system collaborative filtering time heterogeneous feedback recurrent neural network gated recurrent unit(GRU) user space transformation

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