Slanderous user detection with modified recurrent neural networks in recommender system

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.