Social Trust Aware Item Recommendation for Implicit Feedback  

作　　者：郭磊 马军 姜浩然 陈竹敏 刑长明 

机构地区：[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

出　　处：《Journal of Computer Science & Technology》2015年第5期1039-1053,共15页计算机科学技术学报（英文版）

基　　金：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.

摘　　要：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 category- sensitive 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 SocialBPRcawn 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 SocialBPRcawR in terms of AUC （area under the receiver operating characteristic curve）.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 category- sensitive 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 SocialBPRcawn 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 SocialBPRcawR in terms of AUC （area under the receiver operating characteristic curve）.

关 键 词：social recommendation matrix factorization random walk Bayesian personalized ranking 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论] F270[自动化与计算机技术—计算机科学与技术]