期刊
ACM TRANSACTIONS ON INFORMATION SYSTEMS
卷 37, 期 2, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3295499
关键词
POI recommendation; location-based social networks; spatiotemporal aware; contextual modeling
资金
- NSFC [61572376]
- 111 project [B07037]
- ARC Discovery Early Career Researcher Award [DE160100308]
- ARC Discovery Project [DP170103954]
The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the user's decision-making for choosing a POI to visit. In this article, we focus on the spatiotemporal context-aware POI recommendation, which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a spatiotemporal context-aware and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a transition space where spatiotemporal contexts (i.e., a pair) are modeled as translation vectors operating on users and POIs. We further develop a series of strategies to exploit various correlation information to address the data sparsity and cold-start issues for new spatiotemporal contexts, new users, and new POIs. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate that our STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据