Computer Science ›› 2021, Vol. 48 ›› Issue (7): 172-177.doi: 10.11896/jsjkx.200600077

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

Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning

ZHAN Wan-jiang1, HONG Zhi-lin1, FANG Lu-ping1, WU Zhe-fu1, LYU Yue-hua2   

  1. 1 College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China
    2 Zhejiang Institute of Science and Technology Information,Hangzhou 310006,China
  • Received:2020-06-12 Revised:2020-11-26 Online:2021-07-15 Published:2021-07-02
  • About author:ZHAN Wan-jiang,born in 1994,postgraduate.His main research interests include recommendation system and data mining.(
    LYU Yue-hua,born in 1978,master.His main research interests include data mining and artificial intelligence.
  • Supported by:
    Natural Science Foundation of Zhejiang Province(LY18F010025).

Abstract: The recommendation system can recommend relevant information and commodities to the user according to the user’s hobbies and purchase behavior.As user-generated content UGC gradually becomes the mainstream of current Web applications,recommendations based on UGC have also received widespread attention.Different from the binary interaction between user and item in traditional recommendation,the existing UGC recommendation adopts collaborative filtering method to propose a ternary interaction between consumer,item and producer,thereby improving the accuracy of recommendation,but most of the algorithms focus on the recommended performance and ignore the research on robustness.Therefore,by combining the ideas of adversarial learning and collaborative filtering,a collaborative filtering recommendation algorithm based on adversarial learning is proposed.First,the adversarial disturbance is added to the ternary relationship model parameters to make the performance of the model the worst.At the same time,the adversarial learning method is used to train the model to achieve the purpose of improving the robustness of the recommendation model.Secondly,an efficient algorithm is designed used to transform the parameters required by the model.Finally,it is tested on two public data sets generated by Reddit and Pinterest.The experimental results show that under the same parameter settings,compared with the existing algorithms,the AUC,Precision and Recall indicators of the proposed algorithm have been significantly improved,verifying its feasibility and effectiveness.The algorithm not only enhances the recommendation performance,but also improves the robustness of the model.

Key words: Adversarial learning, Collaborative filtering, Matrix factorization, Recommendation system, User-generated content

CLC Number: 

  • TP301.6
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