›› 2015, Vol. 30 ›› Issue (5): 1039-1053.doi: 10.1007/s11390-015-1580-8

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Social Media Processing • Previous Articles     Next Articles

Social Trust Aware Item Recommendation for Implicit Feedback

Lei Guo1,2(郭磊), Jun Ma2*(马军), Senior Member, CCF, Member, ACM, IEEE Hao-Ran Jiang3(姜浩然), Zhu-Min Chen2(陈竹敏), Senior Member, CCF, Member, ACM, Chang-Ming Xing4(邢长明)   

  1. 1 School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China;
    2 School of Computer Science and Technology, Shandong University, Jinan 250101, China;
    3 Bureau of Information Technology, Shandong Post Company, Jinan 250101, China;
    4 School of Continuing Education, Shandong University of Finance and Economics, Jinan 250101, China
  • Received:2014-11-10 Revised:2015-07-15 Online:2015-09-05 Published:2015-09-05
  • Contact: Jun Ma E-mail:majun@sdu.edu.cn
  • About author:Lei Guo received his Ph.D. degree in computer architecture from Shandong University, Jinan, in 2015. Now he works at the School of Management Science and Engineering, Shandong Normal University, Jinan. His research interests include information retrieval, social network and recommender system.
  • Supported by:

    This work is supported by the National Natural Science Foundation of China under Grant Nos. 61272240, 60970047, 61103151 and 71301086, the Doctoral Fund of Ministry of Education of China under Grant No. 20110131110028, the Natural Science Foundation of Shandong Province of China under Grant No. ZR2012FM037, and the Excellent Middle-Aged and Youth Scientists of Shandong Province of China under Grant No. BS2012DX017.

PDF

2630

Social trust aware recommender systems have been well studied in recent years. However, most of existing methods focus on the recommendation scenarios where users can provide explicit feedback to items. But in most cases, the feedback is not explicit but implicit. Moreover, most of trust aware methods assume the trust relationships among users are single and homogeneous, whereas trust as a social concept is intrinsically multi-faceted and heterogeneous. Simply exploiting the raw values of trust relations cannot get satisfactory results. Based on the above observations, we propose to learn a trust aware personalized ranking method with multi-faceted trust relations for implicit feedback. Specifically, we first introduce the social trust assumption——a user's taste is close to the neighbors he/she trusts——into the Bayesian Personalized Ranking model. To explore the impact of users' multi-faceted trust relations, we further propose a categorysensitive random walk method CRWR to infer the true trust value on each trust link. Finally, we arrive at our trust strength aware item recommendation method SocialBPRCRWR by replacing the raw binary trust matrix with the derived real-valued trust strength. Data analysis and experimental results on two real-world datasets demonstrate the existence of social trust influence and the effectiveness of our social based ranking method SocialBPRCRWR in terms of AUC (area under the receiver operating characteristic curve).

[1] Jin R, Chai J Y, Si L. An automatic weighting scheme for collaborative filtering. In Proc. the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2004, pp.337-344.

[2] Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th International Conference on World Wide Web, May 2001, pp.285-295.

[3] Ma H, Yang H, Lyu M R, King I. SoRec:Social recommendation using probabilistic matrix factorization. In Proc. the 17th ACM Conference on Information and Knowledge Management, October 2008, pp.931-940.

[4] Ma H, King I, Lyu M R. Learning to recommend with social trust ensemble. In Proc. the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2009, pp.203-210.

[5] Noel J, Sanner S, Tran K N, Christen P, Xie L, Bonilla E V, Abbasnejad E, Penna N D. New objective functions for social collaborative filtering. In Proc. the 21st International Conference on World Wide Web, April 2012, pp.859-868.

[6] Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR:Bayesian personalized ranking from implicit feedback. In Proc. the 25th Conference on Uncertainty in Artificial Intelligence, June 2009, pp.452-461.

[7] Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks. In Proc. the 4th ACM Conference on Recommender Systems, September 2010, pp.135-142.

[8] Hofmann T. Collaborative filtering via Gaussian probabilistic latent semantic analysis. In Proc. the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28-August 1, 2003, pp.259-266.

[9] Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems, Platt J C, Koller D, Singer Y, Roweis S T (eds.), Curran Associates, Inc., 2008, pp.1257-1264.

[10] Shi Y, Larson M, Hanjalic A. List-wise learning to rank with matrix factorization for collaborative filtering. In Proc. the 4th ACM Conference on Recommender Systems, September 2010, pp.269-272.

[11] Xue G, Lin C, Yang Q, Xi W, Zeng H, Yu Y, Chen Z. Scalable collaborative filtering using cluster-based smoothing. In Proc. the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2005, pp.114-121.

[12] Jamali M, Ester M. TrustWalker:A random walk model for combining trust-based and item-based recommendation. In Proc. the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28-July 1, 2009, pp.397-406.

[13] Yang B, Lei Y, Liu D, Liu J. Social collaborative filtering by trust. In Proc. the 23rd International Joint Conference on Artificial Intelligence, August 2013, pp.2747-2753.

[14] Jiang M, Cui P, Liu R, Yang Q, Wang F, Zhu W, Yang S. Social contextual recommendation. In Proc. the 21st ACM International Conference on Information and Knowledge Management, October 29-November 2, 2012, pp.45-54.

[15] Huang S, Ma J, Cheng P, Wang S. A hybrid multigroup coclustering recommendation framework based on information fusion. ACM Transactions on Intelligent Systems and Technology, 2015, 6(2):27:1-27:22.

[16] Ma H. An experimental study on implicit social recommendation. In Proc. the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28-August 1, 2013, pp.73-82.

[17] Yang X, Steck H, Guo Y, Liu Y. On top-k recommendation using social networks. In Proc. the 6th ACM Conference on Recommender Systems, September 2012, pp.67-74.

[18] Jamali M, Ester M. Using a trust network to improve top- N recommendation. In Proc. the 3rd ACM Conference on Recommender Systems, October 2009, pp.181-188.

[19] Yuan Q, Chen L, Zhao S. Factorization vs. regularization:Fusing heterogeneous social relationships in top-N recommendation. In Proc. the 5th ACM Conference on Recommender Systems, October 2011, pp.245-252.

[20] Du L, Li X, Shen Y. User graph regularized pairwise matrix factorization for item recommendation. In Lecture Notes in Computer Science 7121, Tang J, King I, Chen L, Wang J (eds.), Springer Berlin Heidelberg, 2011, pp.372-385.

[21] Krohn-Grimberghe A, Drumond L, Freudenthaler C, Schmidt-Thieme L. Multi-relational matrix factorization using Bayesian personalized ranking for social network data. In Proc. the 5th ACM International Conference on Web Search and Data Mining, February 2012, pp.173-182.

[22] Pan W, Chen L. GBPR:Group preference based Bayesian personalized ranking for one-class collaborative filtering. In Proc. the 23rd International Joint Conference on Artificial Intelligence, August 2013, pp.2691-2697.

[23] Matsuo Y, Yamamoto H. Community gravity:Measuring bidirectional effects by trust and rating on online social networks. In Proc. the 18th International Conference on World Wide Web, April 2009, pp.751-760.

[24] Tang J, Gao H, Liu H. mTrust:Discerning multi-faceted trust in a connected world. In Proc. the 5th ACM International Conference on Web Search and Data Mining, February 2012, pp.93-102.

[25] Yang X, Steck H, Liu Y. Circle-based recommendation in online social networks. In Proc. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2012, pp.1267-1275.

[26] Friedkin N E. A Structural Theory of Social Influence. Cambridge University Press, 1998.

[27] Ma H, Zhou D, Liu C, Lyu M R, King I. Recommender systems with social regularization. In Proc. the 4th ACM International Conference on Web Search and Data Mining, February 2011, pp.287-296.

[28] Marden J I. Analyzing and Modeling Rank Data. CRC Press, 1996.

[29] Tang J, Gao H, Liu H, Sarma A D. eTrust:Understanding trust evolution in an online world. In Proc. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2012, pp.253-261.

[30] Breese J S, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. the 14th Conference on Uncertainty in Artificial Intelligence, July 1998, pp.43-52.

[31] Herschtal A, Raskutti B. Optimising area under the ROC curve using gradient descent. In Proc. the 21st International Conference on Machine Learning, July 2004.

[32] Pan R, Zhou Y, Cao B, Liu N N, Lukose R, Scholz M, Yang Q. One-class collaborative filtering. In Proc. the 8th IEEE International Conference on Data Mining, December 2008, pp.502-511.

[33] Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In Proc. the 8th IEEE International Conference on Data Mining, December 2008, pp.263- 272.
No related articles found!
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] Zhang Bo; Zhang Ling;. Statistical Heuristic Search[J]. , 1987, 2(1): 1 -11 .
[2] Meng Liming; Xu Xiaofei; Chang Huiyou; Chen Guangxi; Hu Mingzeng; Li Sheng;. A Tree-Structured Database Machine for Large Relational Database Systems[J]. , 1987, 2(4): 265 -275 .
[3] Lin Qi; Xia Peisu;. The Design and Implementation of a Very Fast Experimental Pipelining Computer[J]. , 1988, 3(1): 1 -6 .
[4] Sun Chengzheng; Tzu Yungui;. A New Method for Describing the AND-OR-Parallel Execution of Logic Programs[J]. , 1988, 3(2): 102 -112 .
[5] Wang Nengbin; Liu Xiaoqing; Liu Guangfu;. A Software Tool for Constructing Traditional Chinese Medical Expert Systems[J]. , 1988, 3(3): 214 -220 .
[6] Zhang Bo; Zhang Tian; Zhang Jianwei; Zhang Ling;. Motion Planning for Robots with Topological Dimension Reduction Method[J]. , 1990, 5(1): 1 -16 .
[7] Zhao Ming;. 2-D EAG Method for the Recognition of Hand-Printed Chinese Characters[J]. , 1990, 5(4): 319 -328 .
[8] Wang Dingxing; Zheng Weimin; Du Xiaoli; Guo Yike;. On the Execution Mechanisms of Parallel Graph Reduction[J]. , 1990, 5(4): 333 -346 .
[9] Cai Shijie; Zhang Fuyan;. A Fast Algorithm for Polygon Operations[J]. , 1991, 6(1): 91 -96 .
[10] Zhou Quan; Wei Daozheng;. A Complete Critical Path Algorithm for Test Generation of Combinational Circuits[J]. , 1991, 6(1): 74 -82 .

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

 Home
Editorial Board
Author Guidelines
Subscription		 Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved