ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2018, Vol. 55 ›› Issue (10): 2291-2306.doi: 10.7544/issn1000-1239.2018.20170489

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Research Progress of Recommendation Technology in Location-Based Social Networks

Jiao Xu1,2,3, Xiao Yingyuan1,2, Zheng Wenguang1,2, Zhu Ke1,2   

  1. 1(Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology (Tianjin University of Technology), Tianjin 300384);2(Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384);3(Faculty of Fundamental Courses, Tianjin Foreign Studies University, Tianjin 300204)
  • Online:2018-10-01

Abstract: The rapid development of mobile Internet technology, positioning technology and wireless sensor technology has endowed the smart terminal more powerful features and applications. Location-based social networks (LBSNs) and its services have emerged and advanced rapidly. Location data both bridges the gap between the physical and digital worlds and enables deeper understanding of user preferences and behaviors. The location-based and personalized recommendation service in accordance with users’ interests has become dramatically vital in location-based social networks and has widely received attention in both academia and industry. Currently, it is becoming a new research hotspot in the field of recommendation system and social networks. In this paper, we aim at offering a literature review of the former contributions on this program and exploring the relations within the former achievements. We firstly discuss the new properties and challenges that location brings to recommendation systems for LBSNs. Then, we systematically introduce the location-based social network recommendation service from three aspects: the objective, methodology and the major methods for evaluating. We classify recommendation objectives into four categories: location recommendations, friend & companion recommendations, local expert discovery and activity recommendations. According to the use of data set types, location recommendations and friend & companion recommendations are classified. Finally, we point out the possible research directions of this area in the future and arrive at the conclusion of this survey.

Key words: location-based social networks, recommender systems, point of interest, heterogeneous networks, social media

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