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	Joint modeling of user check-in behaviors for point-of-interest recommendation
	Yin, Hongzhi ; Zhou, Xiaofang ; Shao, Yingxia ; Wang, Hao ; Sadiq, Shazia
	2015
英文摘要	Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting locations, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, a growing line of research has exploited the temporal effect, geographical-social influence, content effect and word-of-mouth effect. However, current research lacks an integrated analysis of the joint effect of the above factors to deal with the issue of data-sparsity, especially in the out-of-town recommendation scenario which has been ignored by most existing work. In light of the above, we propose a joint probabilistic generative model to mimic user check-in behaviors in a process of decision making, which strategically integrates the above factors to effectively overcome the data sparsity, especially for out-of-town users. To demonstrate the applicability and flexibility of our model, we investigate how it supports two recommendation scenarios in a unified way, i.e., home-town recommendation and out-of-town recommendation. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets in terms of both recommendation effectiveness and efficiency, and the experimental results show its superiority over other competitors. ? 2015 ACM.; EI; 1631-1640; 19-23-Oct-2015
语种	英语
出处	24th ACM International Conference on Information and Knowledge Management, CIKM 2015
DOI标识	10.1145/2806416.2806500
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436554]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yin, Hongzhi,Zhou, Xiaofang,Shao, Yingxia,et al. Joint modeling of user check-in behaviors for point-of-interest recommendation. 2015-01-01.

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