Friendship prediction model based on factor graphs integrating geographical location. Issue 3 (17th July 2020)

• Record Type: Journal Article
• Authors: Chen, Liang; Qiao, Shaojie; Han, Nan; Yuan, Chang‐an; Song, Xuejiang; Huang, Ping; Xiao, Yueqiang
• Is Part Of: CAAI transactions on intelligence technology. Volume 5:Issue 3(2020)
• Page Start: 193
• Page End: 199
• Publication Date: 2020-07-17
• Subjects: graph theory -- social networking (online) -- recommender systems -- information retrieval; friendship prediction model -- network services -- location‐based systems -- social network -- recommendation services -- structure theory -- trajectory similarity -- activity patterns -- movement behaviour -- factor graph model -- location‐based social networks -- geographical location information -- geographical location recommendation; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2020.0033
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : With the development of network services and location‐based systems, many mobile applications begin to use users' geographical location to provide better services. In terms of social networks, geographical location is actively shared by users. In some applications with recommendation services, before the geographical location recommendation is provided, the authors have to obtain user's permission. This kind of social network integrated with geographical location information is called location‐based social networks (abbreviate for LBSNs). In the LBSN, each user has location information when he or she checked in hotels or feature spots. Based on this information, they can identify user's trajectory of movement behaviour and activity patterns. In general, if there is friendship between two users, their trajectories in reality are likely to be similar. In this study, according to user's geographical location information over a period of time, they explore whether there exists friendly relationship between two users based on trajectory similarity and the structure theory of graphs. In particular, they propose a new factor function and a factor graph model based on user's geographical location to predict the friendship between two users in the real LBSN.