Objectives and state-of-the-Art of location-Based social network recommender systems

Document Type

Article

Publication Date

1-31-2019

Abstract

Because of the widespread adoption of GPS-enabled devices, such as smartphones and GPS navigation devices, more and more location information is being collected and available. Compared with traditional ones (e.g., Amazon, Taobao, and Dangdang), recommender systems built on location-based social networks (LBSNs) have received much attention. The former mine users’ preferences through the relationship between users and items, e.g., online commodity, movies and music. The latter add location information as a new dimension to the former, hence resulting in a three-dimensional relationship among users, locations, and activities. In this article, we summarize LBSN recommender systems from the perspective of such a relationship. User, activity, and location are called objects, and recommender objectives are formed and achieved by mining and using such 3D relationships. From the perspective of the 3D relationship among these objects, we summarize the state-of-the-art of LBSN recommender systems to fulfill the related objectives. We finally indicate some future research directions in this area.

Identifier

85042503753 (Scopus)

Publication Title

ACM Computing Surveys

External Full Text Location

https://doi.org/10.1145/3154526

e-ISSN

15577341

ISSN

03600300

Issue

1

Volume

51

Grant

61672381

Fund Ref

National Natural Science Foundation of China

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