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

基于位置社交网络的个性化兴趣点推荐

Personalized point-of-interest recommendation in location-based social networks

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作者 韩笑峰,牛保宁,杨茸
机构 太原理工大学 计算机科学与技术学院,太原 030024
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文章编号 1001-3695(2019)05-038-1464-05
DOI 10.19734/j.issn.1001-3695.2017.11.0739
摘要 兴趣点(point-of-interest,POI)推荐是基于位置的社交网络(location-based social networks,LBSN)中一项重要的服务。针对目前推荐算法存在的噪声数据影响推荐质量、用户个性化程度低的问题,提出了一种个性化联合推荐算法。提出了引入POI的位置因素去除不可能或可能性较小的POI,形成初步候选集;综合考虑POI的类别、流行度及用户的社会行为,增加用户个性化的程度,提高推荐结果的质量。在Foursquare真实签到数据集上的实验证明了提出的联合推荐算法与目前先进的算法相比,准确率提高11%,召回率提高8%。
关键词 兴趣点推荐; 位置信息; 分类信息; 流行度信息; 社会信息; 位置社交网络
基金项目 国家自然科学基金资助项目(61572345)
国家科技支撑计划资助项目(2015BAH37F01)
本文URL http://www.arocmag.com/article/01-2019-05-038.html
英文标题 Personalized point-of-interest recommendation in location-based social networks
作者英文名 Han Xiaofeng, Niu Baoning, Yang Rong
机构英文名 College of Computer Science & Technology,Taiyuan University of Technology,Taiyuan 030024,China
英文摘要 POI recommendation is an important service in LBSN. For the current recommendation algorithm exists the problems of the noise data affects the recommended quality and low level of user personalization, this paper proposed a personalized joint recommendation algorithm(JRA). JRA initially utilized the locality of user activity area to early filter the POIs which were impossible or less likely to be a result. For the received preliminary candidate set, then it also considered consider category factor and the popularity factor of POI, and the social behavior of the user to further improve the user experience. The experiments on real Foursquare check-in dataset demonstrate that the JRA compared with the current advanced algorithm, the accuracy rate increases by 11%, recall rate increases by 8%.
英文关键词 POI recommendation; position information; categorization information; popularity information; social information; location-based social network(LBSN)
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收稿日期 2017/11/14
修回日期 2017/12/27
页码 1464-1468
中图分类号 TP391
文献标志码 A