ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (10): 2291-2306.doi: 10.7544/issn1000-1239.2018.20170489

• 人工智能 • 上一篇    下一篇



  1. 1(天津市智能计算及软件新技术重点实验室(天津理工大学) 天津 300384);2(计算机视觉与系统省部共建教育部重点实验室(天津理工大学) 天津 300384);3(天津外国语大学基础课教学部 天津 300204) (
  • 出版日期: 2018-10-01
  • 基金资助: 

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

摘要: 随着移动互联网技术、定位技术和无线传感技术的飞速发展以及智能手机的不断普及,基于位置的社会化网络及其带来的应用服务应运而生并得到了迅速的发展.位置数据弥合了物理世界和数字世界之间的鸿沟,使得人们能够更深入地了解用户的偏好和行为.针对用户的兴趣所在,为用户提供基于位置的个性化推荐服务,已成为当前基于位置的社会化网络的一项重要服务,得到工业界和学术界的广泛重视,正成为推荐系统和社会化网络研究领域的一个新的研究热点.从推荐对象、推荐方法和评价方法3个方面对基于位置的社会化网络推荐技术进行概括、比较与分析;在此基础上,对这一研究领域未来可能的研究方向进行了总结与展望.

关键词: 基于位置的社会化网络, 推荐系统, 兴趣点, 异构网络, 社交媒体

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