GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network
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GARG: Anonymous Recommendation of Point‑of‑Interest in Mobile Networks by Graph Convolution Network Shiwen Wu1,2 · Yuanxing Zhang2 · Chengliang Gao2 · Kaigui Bian2 · Bin Cui1,2,3 Received: 27 February 2020 / Revised: 3 June 2020 / Accepted: 2 July 2020 © The Author(s) 2020
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 pointof-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.
1 Introduction In the past decade, 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 * Bin Cui [email protected] Shiwen Wu [email protected] Yuanxing Zhang [email protected] Chengliang Gao [email protected] Kaigui Bian [email protected] 1
Room 1326, Science Building No.1, Peking University, No.5 YiHeYuan Road, Haidian District, Beijing, China
2
National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China
3
Key Lab of High Confidence Software Technologies (MOE), Department of Computer Science, Peking University, Beijing, China
reviewing service and check-in service, which greatly facilitate the process for people to acquire the information and to access the wanted service. Among all of LBS applications, one of the key techniques is the next Point-of-Interest (POI) recommendation that seeks to accurately predict the POI where a user is interested in or she/he may visit in the future[1]. The spatial information (i.e., the located latitude–longitudes) and temporal information (i.e., timestamps) play a very important role in the next 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 re
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