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02.05.2018

Leveraging multi-aspect time-related influence in location recommendation

verfasst von: Saeid Hosseini, Hongzhi Yin, Xiaofang Zhou, Shazia Sadiq, Mohammad Reza Kangavari, Ngai-Man Cheung

Erschienen in: World Wide Web | Ausgabe 3/2019

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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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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 z_d and 24 for z_h).

http://www.public.asu.edu/~hgao16/

https://snap.stanford.edu/data/loc-brightkite.html

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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Titel: Leveraging multi-aspect time-related influence in location recommendation
verfasst von: Saeid Hosseini
Hongzhi Yin
Xiaofang Zhou
Shazia Sadiq
Mohammad Reza Kangavari
Ngai-Man Cheung
Publikationsdatum: 02.05.2018
Verlag: Springer US
Erschienen in: World Wide Web / Ausgabe 3/2019
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-018-0573-2

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