Time-aware point-of-interest recommendation
Time-aware point-of-interest recommendation
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DOI：
10.1145/2484028.2484030
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Quan Yuan;G. Cong;Zongyang Ma;Aixin Sun;N. Magnenat-Thalmann
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文献类型：
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作者：
Quan Yuan;G. Cong;Zongyang Ma;Aixin Sun;N. Magnenat-Thalmann
来自快速增长的基于位置的社交网络 (LBSN) 的大量用户签到数据的可用性为用户提供了许多重要的位置感知服务。兴趣点（POI）推荐就是此类服务之一，即推荐用户以前没有去过的地方。最近已经提出了几种用于推荐服务的技术。然而，现有的工作还没有考虑 LBSN 中 POI 推荐的时间信息。我们认为时间在 POI 推荐中起着重要作用，因为大多数用户倾向于在一天中的不同时间访问不同的地方，例如中午去餐厅，晚上去酒吧。在本文中，我们定义了一个新问题，即时间感知的 POI 推荐，在一天中的指定时间为给定用户推荐 POI。为了解决这个问题，我们开发了一种能够合并时间信息的协作推荐模型。此外，根据用户倾向于访问附近 POI 的观察，我们通过考虑地理信息进一步增强了推荐模型。我们在两个真实世界数据集上的实验结果表明，所提出的方法大大优于最先进的 POI 推荐方法。
The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, \eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.