Location recommendation methods on location-based social networks (LBSN) discover the locational preference of users along with their spatial movement patterns from users’ check-ins and provide users with recommendations of unvisited places. The growing popularity of LBSNs and abundance of shared location information has made location recommendation an active research area in the recent years. However, the existing methods suffer from one or more deficiencies such as data sparsity, cold-start users, ignoring users’ specific spatial and temporal behaviors, not utilizing the shared behaviors of the users. In this paper, we propose a novel location recommendation method, namely Behavior-based Location Recommendation (BLR). BLR recommends a location to a user based on the users’ repetitive behaviors and behaviors of similar users. Additionally, to better integrate the spatial information, BLR has two spatial components, a user-based spatial component to find the spatial preferences of the user, and a behavior-based spatial component to find locations of interest for different behaviors. Experimental studies on three real-world datasets show that BLR produces better location recommendations and can effectively address data sparsity and cold-start problems.

中文翻译：

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基于位置的社交网络上基于行为的位置推荐

基于位置的社交网络（LBSN）上的位置推荐方法可从用户的签到中发现用户的位置偏好及其空间移动模式，并向用户提供未访问地点的推荐。LBSN的日益普及和大量共享的位置信息使位置推荐成为近年来的活跃研究领域。但是，现有方法存在一个或多个缺陷，例如数据稀疏，冷启动用户，忽略用户的特定空间和时间行为，不利用用户的共享行为。在本文中，我们提出了一种新颖的位置推荐方法，即基于行为的位置推荐（BLR）。BLR根据用户的重复行为和类似用户的行为向用户推荐位置。另外，为了更好地整合空间信息，BLR具有两个空间成分，一个用于发现用户空间偏好的基于用户的空间成分，以及一个用于针对不同行为查找感兴趣位置的基于行为的空间成分。对三个真实数据集的实验研究表明，BLR可以提供更好的位置建议，并且可以有效解决数据稀疏和冷启动问题。