A Top-N-Balanced Sequential Recommendation Based on Recurrent Network

Zhenyu ZHAO  Ming ZHU  Yiqiang SHENG  Jinlin WANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.4   pp.737-744
Publication Date: 2019/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018DAP0003
Type of Manuscript: Special Section PAPER (Special Section on Data Engineering and Information Management)
Category: 
Keyword: 
top-N recommendation,  sequential recommendation,  recurrent neural network,  word embedding,  item embedding,  time dependent,  cold start,  

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Summary: 
To solve the low accuracy problem of the recommender system for long term users, in this paper, we propose a top-N-balanced sequential recommendation based on recurrent neural network. We postulated and verified that the interactions between users and items is time-dependent in the long term, but in the short term, it is time-independent. We balance the top-N recommendation and sequential recommendation to generate a better recommender list by improving the loss function and generation method. The experimental results demonstrate the effectiveness of our method. Compared with a state-of-the-art recommender algorithm, our method clearly improves the performance of the recommendation on hit rate. Besides the improvement of the basic performance, our method can also handle the cold start problem and supply new users with the same quality of service as the old users.