Leveraging multi-aspect time-related influence in location recommendation

• Published in: World wide web (Bussum) Vol. 22; no. 3; pp. 1001 - 1028
• Main Authors: Hosseini, Saeid, Yin, Hongzhi, Zhou, Xiaofang, Sadiq, Shazia, Kangavari, Mohammad Reza, Cheung, Ngai-Man
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2019; Springer Nature B.V
• Subjects: Computer Science; Data mining; Database Management; Filtration; Information retrieval; Information Systems Applications (incl.Internet); Location based services; Operating Systems; Recommender systems; Special Issue on Geo-Social Computing; Hybrid location recommendation; Location-based service; Multi-aspect time-related influence
• ISSN: 1386-145X; 1573-1413
• DOI: 10.1007/s11280-018-0573-2

Point-Of-Interest (POI) recommendation aims to mine a user’s visiting history and find her/his potentially preferred places. Although location recommendation methods have been studied and improved pervasively, the challenges w.r.t employing various influences including temporal aspect still remain unresolved. Inspired by the fact that time includes numerous granular slots (e.g. minute, hour, day, week and etc.), in this paper, we define a new problem to perform recommendation through exploiting all diversified temporal factors. In particular, we argue that most existing methods only focus on a limited number of time-related features and neglect others. Furthermore, considering a specific granularity (e.g. time of a day) in recommendation cannot always apply to each user or each dataset. To address the challenges, we propose a probabilistic generative model, named after Multi-aspect Time-related Influence (MATI) to promote the effectiveness of the location (POI) recommendation task. We also develop an effective optimization algorithm based on Expectation Maximization (EM). Our MATI model firstly detects a user’s temporal multivariate orientation using her check-in log in Location-based Social Networks(LBSNs). It then performs recommendation using temporal correlations between the user and proposed locations. Our method is applicable to various types of the recommendation models and can work efficiently in multiple time-scales. Extensive experimental results on two large-scale LBSN datasets verify the effectiveness of our method over other competitors. Categories and Subject Descriptors: H.3.3 [Information Search and Retrieval]: Information filtering; H.2.8 [Database Applications]: Data mining; J.4 [Computer Applications]: Social and Behavior Sciences