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›› 2015, Vol. 30 ›› Issue (5): 1039-1053.

• Special Section on Social Media Processing •

### 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.

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).

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