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

被引：0
|
作者：
Si Y. [1 ,2 ,3 ]
Zhang F. [1 ,3 ]
Liu W. [1 ,3 ]
机构：
[1] School of Information Science and Engineering, Yanshan University, Qinhuangdao
[2] School of Liren, Yanshan University, Qinhuangdao
[3] Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2020年 / 42卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Gaussian kernel density estimation; Location-based social networks; Membership; Point-Of-Interest (POI) recommendation; User activity;
D O I：
10.11999/JEIT18_dzyxxxb-42-3-678
中图分类号：
学科分类号：
摘要：
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. © 2020, Science Press. All right reserved.
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收藏
页码：678 / 686
页数：8
相关论文
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