Journal of Guangdong University of Technology ›› 2017, Vol. 34 ›› Issue (03): 43-48.doi: 10.12052/gdutxb.170020

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Research on Implicit Trust Relationship Aware Recommendation Algorithm

Hu Hui-cheng, Chen Ping-hua   

  1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2017-01-25 Online:2017-05-09 Published:2017-05-09

Abstract:

Collaborative filtering algorithm suffers from data sparsity and cold start, and explicit trust is more difficult to obtain and sparse. In order to solve these problems and improve the accuracy of recommendation systems, a matrix factorization recommendation algorithm is proposed integrating implicit trust information of users. This algorithm calculates implicit trust relationship between users by using Pearson correlation coefficient and factor of trust, and then integrates implicit trust information into a matrix factorization model to predict the ratings. Experimental results show that the algorithm has better recommendation accuracy.

Key words: implicit trust, matrix factorization, recommendation algorithm

CLC Number: 

  • TP311

[1] FUNK S. Netflix update:try this at home[EB/OL]. (2011)[2017-01-20]. http://sifter.org/~simon/journal/20061211.html.
[2] SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization[J]. Advances in Neural Information Processing Systems, 2007:1257-1264.
[3] LEE D D, SEUNG H S. Algorithms for non-negative matrix factorization[J]. Proceedings of Advances in Neural and Information Processing Systems, 2000, 32(6):556-562.
[4] YANG B, LEI Y, LIU D, et al. Social collaborative filtering by trust[C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence.[S.l.]:AAAI Press, 2013:2747-2753.
[5] JAMALI M, ESTER M. A matrix factorization technique with trust propagation for recommendation in social networks[C]//Proceedings of the Fourth ACM Conference on Recommender Systems, Recsys 2010, Barcelona, Spain:[s.n.], 2010:135-142.
[6] 刘英南, 谢瑾奎, 张家利, 等. 社交网络中基于信任的推荐算法[J]. 小型微型计算机系统, 2015, 36(6):1165-1170. LIU Y N, XIE J K, ZHANG J L, et al. Recommendation algorithm based on trust in social network[J]. Journal of Chinese Computer Systems, 2015, 36(6):1165-1170.
[7] 肖晓丽, 钱娅丽, 李旦江, 等. 基于用户兴趣和社交信任的聚类推荐算法[J]. 计算机应用, 2016, 36(5):1273-1278. XIAO X L, QIAN Y L, LI D J, et al. Clustering recommendation algorithm based on user interest and social trust[J]. Journal of Computer Applications, 2016, 36(5):1273-1278.
[8] 杜永萍, 黄亮, 何明. 融合信任计算的协同过滤推荐方法[J]. 模式识别与人工智能, 2014, 27(5):417-425. DU Y P, HUANG L, HE M. Collaborative filteration recommendation algorithm based on trust computation[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(5):417-425.
[9] GUO G B. Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems[C]//Proceedings of the 7th ACM Conference on Recommender Systems. Hong Kong:ACM, 2013:451-454.
[10] GUO G B, ZHANG J, THALMANN D, et al. From ratings to trust:an empirical study of implicit trust in recommender systems[C]//Proceedings of the 29th Annual ACM Symposium on Applied Computing. Gyeongju, Korea:ACM, 2014:248-253.
[11] O'DONOVAN J, SMYTH B. Trust in recommender systems[C]//Proceedings of the 2005 International Conference on Intelligent User Interfaces. San Diego, California, USA:ACM, 2005.
[12] KOREN Y. Factorization meets the neighborhood:a multifaceted collaborative filtering model[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, USA:ACM, 2008:426-434.
[13] GUO G, 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 Twenty-Ninth AAAI Conference on Artificial Intelligence.[S.l.]:AAAI Press, 2015:123-129.
[14] 郭艳红, 邓贵仕, 雒春雨. 基于信任因子的协同过滤推荐算法[J]. 计算机工程, 2008, 34(20):1-3. GUO Y H, DENG G S, LUO C Y. Collaborative filtering recommendation algorithm based on factor of Trust[J]. Computer Engineering, 2008, 34(20):1-3.
[15] PAPAGELIS M, PLEXOUSAKIS D, KUTSURAS T. Alleviating the sparsity problem of collaborative filtering using trust inferences[C]//Trust Management, Third International Conference, iTrust 2005. Paris, France:Springer, 2005:224-239.
[16] SOTOS A, VANHOOF S, VAN DEN NOORTGATE W, et al. The transitivity misconception of pearson's correlation coefficient[J]. Statistics Education Research Journal, 2009, 8(2):33-55.
[17] GUO G B, ZHANG J, YORKE-SMITH N. A novel Bayesian similarity measure for recommender systems[C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence. Beijing, China:AAAI, 2013:2619-2625.

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