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 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
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Notes
A temporal slot, scale, and dimension (e.g. Hour, Day and etc.) are used interchangeably in this paper unless noted otherwise.
We created matrices of h*h using LINQ queries in which h is the number of slots in each temporal scale (i.e. 7 for zd and 24 for zh).
We used Microsoft SQL Server 2012 relational databases. In expense of the disk space, both non-clustered and clustered indexes which were advised via Microsoft SQL Server Profiler accelerated the process speed exceptionally.
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This work was partially supported by both ST Electronics and the National Research Foundation(NRF), Prime Minister’s Office, Singapore under Corporate Laboratory @ University Scheme (Programme Title: STEE Infosec - SUTD Corporate Laboratory).
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This article belongs to the Topical Collection: Special Issue on Geo-Social Computing
Guest Editors: Guandong Xu, Wen-Chih Peng, Hongzhi Yin, Zi (Helen) Huang
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Hosseini, S., Yin, H., Zhou, X. et al. Leveraging multi-aspect time-related influence in location recommendation. World Wide Web 22, 1001–1028 (2019). https://doi.org/10.1007/s11280-018-0573-2
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DOI: https://doi.org/10.1007/s11280-018-0573-2