Points-of-interest Recommendation Algorithm Based on LBSN in Edge Computing Environment

Keyan Cao, Jingjing Guo, Gongjie Meng, Haoli Liu, Yefan Liu, Gui Li
2020 IEEE Access  
With the advancement of the Internet of Everything era and the popularity of mobile devices, Location-based Social Networks (LBSN) have penetrated people's lives. People can take advantage of portable edge terminal devices and use the geographic information in LBSN to arrange or adjust their travel plans. However, due to the explosive growth of current Internet applications and users, it has brought greater pressure and operation and maintenance costs to cloud storage. It is a key research
more » ... tion based on location recommendation to accurately obtain the places of interest of users and push them to clients in such a large amount of original data. In order to better process the data generated by edge devices, this paper firstly uses the Rank-FBPR matrix decomposition framework based on social network to analyze the user's personal preference function on the edge server. Then interact with the geographic information stored in the Cloud to cluster the POIs. And embeds the geographic information into the framework to get the candidate points of interest. Finally, the scores of candidate points of interest are predicted using the personal preference function and power law distribution, then a sorted list of points of interest is generated in descending order of scores, and the list is recommended to the target user. This algorithm effectively integrates the time information and geographic information of users' check-in in the LBSN, and proposes a POIs recommendation algorithm that comprehensively considers edge devices and Cloud. The experiments verify the effectiveness of framework from both cold start and non-cold start. The experimental results on the Foursquare and the Yelp datasets show that Rank-FBPR has higher recommendation accuracy and recall than other comparison models, and can adapt to cold start problems. INDEX TERMS LBSN, edge computing, personal preference. I. INTRODUCTION With the rapid development of Internet technology and the continuous popularization of mobile communication devices, Location-based Social Networks (LBSN) have penetrated people's lives. People can use the portable terminal to access the Internet, and use the geographic information and social attributes in LBSN to define the geographic location preferences of users to access points of interest. Users can arrange or adjust work and travel plans in time to achieve the effect of intelligent perception and convenient use of all kinds of information. At the same time, the rapid arrival of the era The associate editor coordinating the review of this manuscript and approving it for publication was Xiaofei Wang . of Cloud computing, big data and IoT has led to the explosive growth trend of network edge devices (such as smart phones, wearable smart devices, etc.) in the past decade, and the increasing demand of mobile users for matching service quality. In addition, the high computing power and accuracy required for points-of-interest recommendation are increasingly not guaranteed. In this case, the centralized processing mode is unable to process the data generated by the edge devices, so the edge calculation comes into being. The edge in edge calculation refers to the calculation and storage of network edge [1], which is opposite to the data center and closer to the user in terms of network distance or geographical distance [2] . In the edge computing model, some or all computing tasks from the original Cloud center are migrated VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
doi:10.1109/access.2020.2979922 fatcat:zaacjuvo35hohceprw54xsqnoq