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

Abstract

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 direction 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.

Point-of-Interest Recommender Systems Based on Location-Based Social Networks: A Survey from an Experimental Perspective

“…We would also like to mention that in some works we have found strange statistics that we believe to be inaccurate. For example, in [21], the authors claim to use a Foursquare dataset but the same statistics can be found in [32,47] for a Gowalla dataset. The statistics reported in [98,113] are also strange as they report more users than check-ins (in this case, for a Foursquare dataset).…”

Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks

“…Those public datasets were widely used in early studies on social networks. Gan and Gao [28], Naserian et al [29], and Cao et al [30] used the Foursquare dataset to verify the applicability of their location-based recommendation systems. Jiao et al [31] used the Gowalla and Foursquare datasets to simulate travel decision-making processes in order to recommend POIs for users.…”

“…Liu et al [76] developed the spatiotemporal dilated convolutional generative network for POI recommendation. Cao et al [30] developed a recommendation system based on edge computing using LBSN data.…”

Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective

“…We would also like to mention that in some works we have found strange statistics that we believe to be inaccurate. For example, in [21], the authors claim to use a Foursquare dataset but the same statistics can be found in [34,53] for a Gowalla dataset. The statistics reported in [122,141] are also strange as they report more users than check-ins (in this case, for a Foursquare dataset).…”