司亚利^{1, 2, 3},
张付志^{1, 3,,},
刘文远^{1, 3}
1.燕山大学信息科学与工程学院 秦皇岛 066004
2.燕山大学里仁学院 秦皇岛 066004
3.河北省计算机虚拟技术与系统集成重点实验室(燕山大学) 秦皇岛 066004
基金项目:国家自然科学基金(61379116, 61772452)，河北省自然科学基金(F2015203046, F2015501105)

详细信息

作者简介:司亚利：女，1981年生，副教授，研究方向为兴趣点推荐系统
张付志：男，1964年生，教授，研究方向为推荐系统
刘文远：男，1968年生，教授，研究方向为物联网系统

通讯作者:张付志　xjzfz@ysu.edu.cn

中图分类号:TP391

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出版历程

收稿日期:2019-04-25
修回日期:2019-10-29
网络出版日期:2019-11-11
刊出日期:2020-03-19

An Adaptive Point-Of-Interest Recommendation Method Based on Check-in Activity and Temporal-Spatial Probabilistic Models

Yali SI^{1, 2, 3},
Fuzhi ZHANG^{1, 3,,},
Wenyuan LIU^{1, 3}
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2. School of Liren, Yanshan University, Qinhuangdao 066004, China
3. Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province (Yanshan University), Qinhuangdao 066004, China
Funds:The National Natural Science Foundation of China (61379116, 61772452), The Natural Science Foundation of Hebei Province (F2015203046, F2015501105)


摘要
摘要:针对现有兴趣点(POI)推荐算法对不同签到特征的用户缺乏自适应性问题，该文提出一种基于用户签到活跃度(UCA)特征和时空(TS)概率模型的自适应兴趣点推荐方法UCA-TS。利用概率统计分析方法提取用户签到的活跃度特征，给出一种用户不活跃和活跃的隶属度计算方法。在此基础上，分别采用结合时间因素的1维幂律函数和2维高斯核密度估计来计算不活跃和活跃特征的概率值，同时融入兴趣点流行度来进行推荐。该方法能自适应用户的签到特征，并能更准确体现用户签到的时间和空间偏好。实验结果表明，该方法能够有效提高推荐精度和召回率。
关键词:基于位置社交网络/
兴趣点推荐/
用户活跃度/
隶属度/
高斯核密度估计
Abstract:Existing Point-Of-Interest (POI) recommendation algorithms lack adaptability for users with different check-in features. To solve this problem, an adaptive POI recommendation method UCA-TS based on User Check-in Activity (UCA) feature and Temporal-Spatial (TS) probabilistic models is proposed. The user check-in activity is extracted using a probabilistic statistical analysis method, and a calculation method of user's inactive and active membership is given. On this basis, one-dimensional power law function and two-dimensional Gaussian kernel density estimation combined with time factor are used to calculate the probability for inactive and active features respectively, and the popularity of POI is incorporated to recommend. This method can adapt to the users' check-in features and reflect the users' check-in temporal-spatial preferences more accurately. The experiments show that the proposed method can effectively improve the recommendation precision and recall.
Key words:Location-based social networks/
Point-Of-Interest (POI) recommendation/
User activity/
Membership/
Gaussian kernel density estimation



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