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2025年4月7日 6:04 星期一
首页 > 过刊浏览>2015年第26卷第6期 >1356-1372. DOI:10.13328/j.cnki.jos.004831
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社会化推荐系统研究
作者:
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


Research on Social Recommender Systems
Author:
  • MENG Xiang-Wu

    MENG Xiang-Wu

    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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  • LIU Shu-Dong

    LIU Shu-Dong

    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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  • ZHANG Yu-Jie

    ZHANG Yu-Jie

    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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  • HU Xun

    HU Xun

    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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  • 摘要
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  • 参考文献 [91]
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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.

    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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孟祥武,刘树栋,张玉洁,胡勋.社会化推荐系统研究.软件学报,2015,26(6):1356-1372

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