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Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction
Qiang Cui1; Yuyuan Tang2; Shu Wu1; Liang Wang1
2019-04
会议名称Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
页码289-301
会议日期14-17 April 2019
会议地点Macau SAR, China
出版者Springer
摘要

Point-of-interest (POI) prediction is a key task in location-based social networks. It captures the user preference to predict POIs. Recent studies demonstrate that spatial influence is significant for prediction. The distance can be converted to a weight reflecting the relevance of two POIs or can be utilized to find nearby locations. However, previous studies almost ignore the correlation between user and distance. When people choose the next POI, they will consider the distance at the same time. Besides, spatial influence varies greatly for different users. In this work, we propose a Distance-to-Preference (Distance2Pre) network for the next POI prediction. We first acquire the user's sequential preference by modeling check-in sequences. Then, we propose to acquire the spatial preference by modeling distances between successive POIs. This is a personalized process and can capture the relationship in user-distance interactions. Moreover, we propose two preference encoders which are a linear fusion and a non-linear fusion. Such encoders explore different ways to fuse the above two preferences. Experiments on two real-world datasets show the superiority of our proposed network.

关键词Poi Sequential Preference Spatial Preference Non-linear
收录类别其他
七大方向——子方向分类知识表示与推理
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/23692
专题模式识别实验室
通讯作者Shu Wu
作者单位1.Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) and University of Chinese Academy of Sciences (UCAS)
2.University of Science and Technology Beijing
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室
推荐引用方式
GB/T 7714
Qiang Cui,Yuyuan Tang,Shu Wu,et al. Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction[C]:Springer,2019:289-301.
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