Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Effective successive POI recommendation inferred with individual behavior and group preference

Language

English

Abstract

The prevalence of smartphones and mobile social networks allow the users to share their location-based life experience much easier. The large amount of data generated in related location-based social networks provides informative cues on user's behaviors and preferences to support personalized location-based services, like point-of-interest (POI) recommendation. Yet achieving accurate personalized POI recommendation is challenging as the data available for each user is highly sparse. In addition, the computational complexity is high due to the large number of users. In this paper, a novel methodology for personalized successive POI recommendation is proposed. First, the preferred successive category of location is predicted using a third-rank tensor computed based on the partially observed transitions between the categories of user's successive locations where the missing transitions are uncovered by inferring the group preference. The group is achieved according to users' demographics and frequently visited locations. Then, a bipartite graph is constructed based on the recommended categories for each user. To obtain the personalized ranking of locations, a distance weighted HITS algorithm is proposed so that the location authority score is updated iteratively according to the visiting frequency of the group and some distance constraints. The proposed two-step approach with the category prediction incorporated aims to boost the location prediction performance via the smoothing and at the same time reduce the complexity. Experimental results obtained based on the real-world location-based social network data show that the proposed approach outperforms the existing state-of-the-art methods by a large margin.

Keywords

Location-based social network, Point-of-interest recommendation, Tensor factorization, Individual behavior, Group preference

Publication Date

10-2016

Source Publication Title

Neurocomputing

Volume

210

Start Page

174

End Page

184

Publisher

Elsevier

Peer Reviewed

1

Funder

This work is partially supported by National Program on Key Basic Research Project under Grant No. 2013CB329605 and NSFC under Grant No. 61300178.

DOI

10.1016/j.neucom.2015.10.146

ISSN (print)

09252312

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