Abstract
The advances of mobile equipment and localization techniques put forward the accuracy of the location-based service (LBS) in mobile networks. One core issue for the industry to exploit the economic interest of the LBSs is to make appropriate point-of-interest (POI) recommendation based on users’ interests. Today, the LBS applications expect the recommender systems to recommend the accurate next POI in an anonymous manner, without inquiring users’ attributes or knowing the detailed features of the vast number of POIs. To cope with the challenge, we propose a novel attentive model to recommend appropriate new POIs for users, namely Geographical Attentive Recommendation via Graph (GARG), which takes full advantage of the collaborative, sequential and content-aware information. Unlike previous strategies that equally treat POIs in the sequence or manually define the relationships between POIs, GARG adaptively differentiates the relevance of POIs in the sequence to the prediction, and automatically identifies the POI-wise correlation. Extensive experiments on three real-world datasets demonstrate the effectiveness of GARG and reveal a significant improvement by GARG on the precision, recall and mAP metrics, compared to several state-of-the-art baseline methods.
Highlights
In the past decade, the advance of mobile computing techniques has led to the widespread popularity of locationbased service (LBS) in mobile networks
We find that Geographical Attentive Recommendation via Graph (GARG) can precisely identify the preferences of users, and yield the POIs that are reachable by the users
Mobile LBS applications have facilitated our daily life, while the data sparsity problem and the lack of POI labels significantly degrade the performance of the common POI recommender systems
Summary
The advance of mobile computing techniques has led to the widespread popularity of locationbased service (LBS) in mobile networks. Many companies have launched LBS applications over mobile devices such as electronic map service, online ride-hailing service, online. The spatial information (i.e., the located latitude–longitudes) and temporal information (i.e., timestamps) play a very important role in the POI recommendation in the LBS applications when compared to the recommendation for common items[2, 3]. Note that people’s trajectories in a short period are always within a small region, and they tend to focus on several main POIs (e.g., home and company) in many daily scenarios. There are three most practical features in the POI recommendation in LBS applications. The collaborative information contributes to the factorization-based POI recommendation strategies, which
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