Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6): 158-164.doi: 10.11896/jsjkx.210500013

• Database & Big Data & Data Science • Previous Articles     Next Articles

Hybrid Recommender System Based on Attention Mechanisms and Gating Network

GUO Liang, YANG Xing-yao, YU Jiong, HAN Chen, HUANG Zhong-hao   

  1. School of Software,Xinjiang University,Urumqi 830008,China
  • Received:2021-05-02 Revised:2021-09-08 Online:2022-06-15 Published:2022-06-08
  • About author:GUO Liang,born in 1997,postgraduate.His main research interests include data mining and recommender system.
    YANG Xing-yao,born in 1984,Ph.D,associate professor, is a member of China Computer Federation.His main research interests include recommender system and trust computing.
  • Supported by:
    National Natural Science Foundation of China(61862060,61966035,61562086),Education Department Project of Xinjiang Uygur Autonomous Region(XJEDU2016S035) and Doctoral Research Start-up Foundation of Xinjiang University(BS150257).

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Abstract: Combining user reviews with user ratings to improve the performance of recommender system is the current mainstream research direction of recommender system.However,when user review data is sparse,the performance of most existing recommender systems will degrade to a certain extent.To solve this problem,this paper proposes a hybrid recommendation system (AMGNRS),which combines attention mechanism and gating networking based recommendation system.It use auxiliary comments generated by like-minded users to alleviate the sparsity of user comments.Firstly,a variety of mixed attention mechanism are combined to impove the feature extracting efficiency of user comments and grading.Then features are extracted by adaptive fusion of gated network,and features most relevant to user preference are selected.Finally,the higher order linear interaction of the neural factorization machine is used to derive the score prediction.By comparing the model with the current model with excellent performance on three real data sets,the results show that the problem of data sparsity is significantly alleviated and the effectiveness of the model is verified.

Key words: Attention mechanism, Collaborative filtering, Gated network, Recommender system, Semantic information

CLC Number: 

  • TP391
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