Leveraging Multi-aspect Time-related Influence in Location Recommendation
This work addresses the challenge of incorporating comprehensive temporal influences in location recommendation for users of Location-based Social Networks, representing an incremental improvement over prior methods.
The paper tackles the problem of Point-Of-Interest recommendation by exploiting diverse temporal factors like minute, hour, day, and week, which existing methods often neglect, and proposes a probabilistic generative model (MATI) that improves recommendation performance over competitors on two large-scale datasets.
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.