Leveraging Multi-aspect Time-related Influence in Location Recommendation

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 rem...

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Bibliographic Details
Published inarXiv.org
Main Authors Hosseini, Saeid, Yin, Hongzhi, Zhou, Xiaofang, Sadiq, Shazia
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.01.2017
Subjects
Algorithms
Location based services
Optimization
Recommender systems
Social networks
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Summary: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. 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 POI recommendation. We also develop a novel 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 adaptable to various types of recommendation systems 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.
ISSN:2331-8422