Leveraging Multi-aspect Time-related Influence in Location Recommendation

TL;DR

This paper introduces MATI, a probabilistic model that leverages multiple time-related factors at various granularities to improve location recommendations by capturing complex temporal influences from user check-in data.

Contribution

The paper proposes a novel multi-aspect time influence model (MATI) that considers diverse temporal features and adapts to different granularities, enhancing POI recommendation accuracy.

Findings

MATI outperforms existing methods on large-scale datasets.

The model effectively captures complex temporal influences.

It demonstrates adaptability across multiple time-scales.

Abstract

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.