Probabilistic Identification of Visited Point-of-Interest for Personalized Automatic Check-in

被引:15
|
作者
Nishida, Kyosuke [1 ]
Toda, Hiroyuki [2 ]
Kurashima, Takeshi [2 ]
Suhara, Yoshihiko [2 ]
机构
[1] NTT Resonant Inc, Minato Ku, Tokyo, Japan
[2] NTT Corp, NTT Serv Evolut Labs, Yokosuka, Kanagawa, Japan
来源
UBICOMP'14: PROCEEDINGS OF THE 2014 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING | 2014年
关键词
Check-in; point of interest; spatial trajectories; Foursquare; hierarchical Bayesian model; LOCATIONS; GPS;
D O I
10.1145/2632048.2632092
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Automatic check-in, which is to identify a user's visited points of interest (POIs) from his or her trajectories, is still an open problem because of positioning errors and the high POI density in small areas. In this study, we propose a probabilistic visited-POI identification method. The method uses a new hierarchical Bayesian model for identifying the latent visited-POI label of stay points, which are automatically extracted from trajectories. This model learns from labeled and unlabeled stay point data (i.e., semi-supervised learning) and takes into account personal preferences, stay locations including positioning errors, stay times for each category, and prior knowledge about typical user preferences and stay times. Experimental results with real user trajectories and POIs of Foursquare demonstrated that our method achieved statistically significant improvements in precision at 1 and recall at 3 over the nearest neighbor method and a conventional method that uses a supervised learning-to-rank algorithm.
引用
收藏
页码:631 / 642
页数:12
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