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Slanderous user detection with modified recurrent neural networks in recommender system

Yuanbo Xu,Yongjian Yang,Jiayu Han,E. Wang,Jingci Ming,Hui Xiong

Published 2019 in Information Sciences

ABSTRACT

Abstract We focus on how to tackle a unique multi-view unsupervised issue: slanderous user detection, with recurrent neural networks to benefit recommender systems. In real-world recommender systems, some consumers always give fake reviews and low ratings to the items they bought on purpose. In order to ensure their profits, these slanderous users make a semantic gap between their ratings and reviews to avoid detection, which makes slanderous user detection a more difficult problem. On some occasions, they give a false low rating with a positive review which confuse recommender systems, and vice versa. To address the above problem, in this paper, we propose a novel recommendation framework: Slanderous user Detection Recommender System (SDRS). In SDRS, we design a Hierarchical Dual-Attention recurrent Neural network (HDAN) with a modified GRU (mGRU) to compute an opinion level for reviews. Then a joint filtering method is proposed to catch the gap between ratings and reviews. With joint filtering, slanderous users can be detected and omitted. Finally, a modified non-negative matrix factorization is proposed to make recommendations. Extensive experiments are conducted in four datasets: Amazon, Yelp, Taobao, and Jingdong, in which the results demonstrate that our proposed method can detect slanderous users and make accurate recommendations in a uniform framework. Also, with slanderous user detection, some state-of-the-art recommendation systems can be benefited.

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