| 社会化推荐系统研究 |
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| 引用本文： | 孟祥武,刘树栋,张玉洁,胡勋.社会化推荐系统研究[J].软件学报,2015,26(6):1356-1372. |
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| 作者姓名： | 孟祥武 刘树栋 张玉洁 胡勋 |
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| 作者单位： | 智能通信软件与多媒体北京市重点实验室(北京邮电大学), 北京 100876;北京邮电大学 计算机学院, 北京 100876,智能通信软件与多媒体北京市重点实验室(北京邮电大学), 北京 100876;北京邮电大学 计算机学院, 北京 100876,智能通信软件与多媒体北京市重点实验室(北京邮电大学), 北京 100876;北京邮电大学 计算机学院, 北京 100876,智能通信软件与多媒体北京市重点实验室(北京邮电大学), 北京 100876;北京邮电大学 计算机学院, 北京 100876 |
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| 基金项目： | 国家自然科学基金(60872051); 北京市教育委员会共建项目 |
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| 摘 要： | 近年来,社会化推荐系统已成为推荐系统研究领域较为活跃的研究方向之一.如何利用用户社会属性信息缓解推荐系统中数据稀疏性和冷启动问题、提高推荐系统的性能,成为社会化推荐系统的主要任务.对最近几年社会化推荐系统的研究进展进行综述,对信任推理算法、推荐关键技术及其应用进展进行前沿概括、比较和分析.最后,对社会化推荐系统中有待深入研究的难点、热点及发展趋势进行展望.
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| 关 键 词： | 推荐系统 协同过滤 信任推理 矩阵分解 因子分解机 |
| 收稿时间： | 2014/4/25 0:00:00 |
| 修稿时间： | 3/9/2015 12:00:00 AM |
Research on Social Recommender Systems |
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| MENG Xiang-Wu,LIU Shu-Dong,ZHANG Yu-Jie and HU Xun.Research on Social Recommender Systems[J].Journal of Software,2015,26(6):1356-1372. |
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| Authors: | MENG Xiang-Wu LIU Shu-Dong ZHANG Yu-Jie and HU Xun |
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| Institution: | Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunication), Beijing 100876, China;School of the Computer Science, Beijing University of Posts and Telecommunication, Beijing 100876, China,Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunication), Beijing 100876, China;School of the Computer Science, Beijing University of Posts and Telecommunication, Beijing 100876, China,Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunication), Beijing 100876, China;School of the Computer Science, Beijing University of Posts and Telecommunication, Beijing 100876, China and Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunication), Beijing 100876, China;School of the Computer Science, Beijing University of Posts and Telecommunication, Beijing 100876, China |
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| Abstract: | Social recommender systems have recently become one of the hottest topics in the domain of recommender systems. The main task of social recommender system is to alleviate data sparsity and cold-start problems, and improve its performance utilizing users' social attributes. This paper presents an overview of the field of social recommender systems, including trust inference algorithms, key techniques and typical applications. The prospects for future development and suggestions for possible extensions are also discussed. |
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| Keywords: | recommender system collaborative filtering trust inference matrix factorization factorization machine |
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