Computer Science ›› 2019, Vol. 46 ›› Issue (10): 27-31.doi: 10.11896/jsjkx.190300388

• Big Data & Data Science • Previous Articles     Next Articles

Recommendation Algorithm with Field Trust and Distrust Based on SVD

ZHANG Qi, LIU Ling, WEN Jun-hao   

  1. (School of Big Data and Software Engineering,Chongqing University,Chongqing 401331,China)
  • Received:2019-03-01 Revised:2019-05-14 Online:2019-10-15 Published:2019-10-21

Abstract: The collaborative filtering algorithms in recommender systems usually suffer from data sparsity or cold-start problems.Although most of the existing social recommendation algorithms can alleviate these problems to a certain extent,they only measure the influence of trust relationship from a single aspect.In order to measure the influence of the social relationship on recommendation prediction more accurately,this paper proposed a novel social recommendation algorithm with field trust and distrust based on singular value decomposition (SVD),named FTDSVD.Based on the SVD algorithm,the trust relationship and distrust relationship information of users is added in order to correct the social relationship,and the global influence of users and the field relevance of trust are considered.Finally,it is compared with the state-of-the-art methods on the Epinions dataset .Experiment results show that the FTDSVD algorithm has obvious effects in improving the recommendation quality and alleviating the cold start problem.

Key words: Distrust relationship, Field correlation, Re-commender system, Singular value decomposition (SVD), Trust recommendation

CLC Number: 

  • TP391
[1]ABBASI M A,TANG J L,LIU H.Trust-aware recommender systems[M].USA:Chapman and Hall/CRC Press,2014:11-12.
[2]CAI H,JIA Y B,HUANG C W.Research of collaborative filtering recommendation based on user trust model[J].CEA,2010,46(35):148-151.(in Chinese)
蔡浩,贾宇波,黄成伟.结合用户信任模型的协同过滤推荐方法研究[J].计算机工程与应用,2010,46(35):148-151.
[3]FUNK S.Netflix update:try this at home[EB/OL].[2019-03-01].http://www.sifter.org/~simon/journal/20061211.html.
[4]WU X W,LIU S D,ZHANG Y J,et al.Research on Social Recommender Systems[J].Journal of Software,2015,26(6):1356-1372.(in Chinese)
孟祥武,刘树栋,张玉洁,等.社会化推荐系统研究[J].软件学报,2015,26(6):1356-1372.
[5]ZHAO S,LIU X M,DUAN Z,et al.A Survey on Social Ties Mining[J].Chinese Journal of Computers,2017,40(3):535-555.
[6]MASSA P.A survey of trust use and modeling in real online systems[M].USA:IGIGlobal,2007:51-83.
[7]YUAN Q,CHEN L,ZHAO S.Factorization vs.regularization:Fusing heterogeneous social relationships in top-N recommendation[C]//Proceedings of the 2011 ACM Conference on Recommender Systems.New York:ACM,2011:245-252.
[8]TANG J,HU X,GAO H,et al.Exploiting local and global social context for recommendation[C]//Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence.Menlo Park,CA:AAAI Press,2013:2712-2718.
[9]TANG J,AGGARWAL C,LIU H.Recommendations in Signed Social Networks[C]//Proceedings of the 25th International Conference on World Wide Web.New York:ACM,2016:31-40.
[10]ABBASSI Z,APERJIS C,HUBERMAN B A.Friends versus the crowd:tradeoffs and dynamics[R].HP Report,Palo Alto:HP Labs,2013.
[11]RAFAILIDIS D.Modeling trust and distrust information in recommender systems via joint matrix factorization with signed graphs[C]//Proceedings of the 31st Annual ACM Symposium on Applied Computing.New York:ACM,2016:1060-1065.
[12]RAFAILIDIS D,CRESTANI F.Learning to Rank with Trust and Distrust in Recommender Systems[C]//Proceedings of the Eleventh ACM Conference on Recommender Systems.New York:ACM,2017:5-13.
[13]GUHA R,KUMAR R,RAGHAVAN P,et al.Propagation of trust and distrust[C]//Proceedings of the 13th International Conference on World Wide Web.New York:ACM,2004:403-411.
[14]TANG J,HU X,LIU H.Is distrust the negation of trust?:the value of distrust in social media[C]//Proceedings of the 25th ACM Conference on Hypertext and Social Media.New York:ACM,2014:185-199.
[15]TANG J,GAO H,LIU H.mTrust:Discerning Multi-Faceted Trust in a Connected World[C]//Proceedings of the Fifth ACM International Conference on Web Search and Data Mining.New York:ACM,2012:93-102.
[16]LIN T H,GAO C,LI Y.Recommender Systems with Characteri-zed Social Regularization[C]//Proceedings of the 27th ACM International Conference on Information and KnowledgeMana-gement.New York:ACM,2018:1767-1770.
[17]GUO G B,ZHANG J,YORKE-SMITH N.TrustSVD:collaborative filtering with both the explicit and implicit influence of user trust and of item ratings[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence.Menlo Park,CA:AAAI Press,2015:123-129.
[18]FORSATI R,MAHDAVI M,SHAMSFARD M,et al.Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation[J].ACM Transactions on Information Systems,2014,32(4):1-38.
[19]RESNICK P,ZECKHAUSER R.Trust Among Strangers in Internet Transactions:Empirical Analysis of eBay’s Reputation System[C]//Baye M R,ed.Advances in Applied Microeconomics.Amsterdam:Elsevier,2002:127-157.
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