《计算机应用研究》|Application Research of Computers

融合社交行为和标签行为的推荐算法研究

Study of recommended algorithm integrating social behavior and labeling behavior

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作者 蒋云,倪静,房宏扬
机构 上海理工大学 管理学院,上海 200093
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文章编号 1001-3695(2019)07-010-1965-05
DOI 10.19734/j.issn.1001-3695.2018.01.0038
摘要 针对传统推荐算法忽略用户社交影响、研究角度不全面和缺乏物理解释等问题,提出一个融合社交行为和标签行为的推荐算法。首先用引力模型计算社交网络中用户节点之间的吸引力来度量用户社交行为的相似性;其次通过标签信息构建用户喜好物体模型,并使用引力公式计算喜好物体之间的引力来度量标签行为的相似性。最后,引入变量融合两方面信息,获取近邻用户,产生推荐。采用Last.fm数据集进行实验研究,结果说明推荐算法的准确率和召回率更高。
关键词 社交行为; 标签行为; 万有引力; 协同过滤
基金项目 国家自然科学基金面上项目(71774111)
本文URL http://www.arocmag.com/article/01-2019-07-010.html
英文标题 Study of recommended algorithm integrating social behavior and labeling behavior
作者英文名 Jiang Yun, Ni Jing, Fang Hongyang
机构英文名 School of Business,University of Shanghai for Science & Technology,Shanghai 200093,China
英文摘要 In view of the traditional recommendation algorithm ignoring the impact of social behavior of users, the incomprehensive research perspective and lack of physical explanation, this paper proposed a recommendation algorithm that integrated social behavior and tagging behavior of users. Firstly, it calculated the attractiveness between user nodes in social network by gravity model to measure the similarity of users' social behavior. Secondly, it constructed the user's favorite object model by label information, also used the gravitation formula to calculate the gravitation between favorite objects to measure the similarity of tagging behavior. Finally, the paper introduced the variables to weigh the proportion of two similar values, and then got the set of neighbors and generated recommendations. Experimental results using Last. fm dataset show that the proposed algorithm has higher precision and recall.
英文关键词 social behavior; labeling behavior; gravitation; collaborative filtering
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收稿日期 2018/1/22
修回日期 2018/3/6
页码 1965-1969
中图分类号 TP301.6
文献标志码 A