计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (7): 1462-1478.DOI: 10.3778/j.issn.1673-9418.2112037

• 综述·探索 • 上一篇    下一篇

基于位置社交网络的兴趣点推荐系统研究综述

陈江美1, 张文德2,+()   

  1. 1.福州大学 经济与管理学院,福州 350108
    2.福州大学 信息管理研究所,福州 350108
  • 收稿日期:2021-12-09 修回日期:2022-02-14 出版日期:2022-07-01 发布日期:2022-07-25
  • 作者简介:陈江美(1995—),女,福建南平人,博士研究生,主要研究方向为商务智能、数据挖掘等。
    CHEN Jiangmei, born in 1995, Ph.D. candidate. Her research interests include business intelligence, data mining, etc.
    张文德(1962—),男,福建福州人,博士,教授,主要研究方向为信息化管理、知识产权等。
    ZHANG Wende, born in 1962, Ph.D., professor. His research interests include information management, intellectual property, etc.
  • 基金资助:
    国家自然科学基金青年项目(61300104);中国高校产学研创新基金新一代信息技术创新项目(2019ITA0103)

Review of Point of Interest Recommendation Systems in Location-Based Social Networks

CHEN Jiangmei1, ZHANG Wende2,+()   

  1. 1. School of Economy and Management, Fuzhou University, Fuzhou 350108, China
    2. Institute of Information Management, Fuzhou University, Fuzhou 350108, China
  • Received:2021-12-09 Revised:2022-02-14 Online:2022-07-01 Published:2022-07-25
  • Supported by:
    the National Natural Science Foundation for Youth of China(61300104);the New Generation Information Technology Innovation Project of Industry-University-Research Innovation Fund in Chinese Universities(2019ITA0103)

摘要:

兴趣点推荐是近年来位置社交网络和推荐系统领域研究的热点之一,了解兴趣点推荐在位置社交网络方面的发展现状,有利于为下一步的研究提供方向。对国内外兴趣点推荐系统的相关文献进行梳理,首先介绍了兴趣点推荐系统的概念,并从影响推荐的因素、推荐方法和推荐存在的问题三方面探讨其与传统推荐的区别。然后提出了兴趣点推荐系统的基本框架,该框架包含了数据来源、推荐方法和算法评价三个核心部分。以该框架为基础,介绍了影响兴趣点推荐的多种因素,归纳了现有的兴趣点推荐算法,总结了算法的评价指标。同时对代表性工作进行了分析介绍,详细总结了各种方法的研究内容与特点,并评价了其优势与不足。最后对该领域所面临的挑战和潜在的研究方向进行了总结与展望,给出了未来的研究趋势和发展方向。

关键词: 位置社交网络, 推荐系统, 兴趣点推荐, 影响因素

Abstract:

Point of interest recommendation is recently one of the hotspots in the field of location-based social networks and recommendation systems. Understanding the research status of the point of interest recommendation in location-based social networks can provide a direction for the next step of work. The recent literatures of the point of interest recommendation systems are analyzed. Firstly, the definition is introduced, and the difference from traditional recommendation is discussed from three aspects: influencing factors, recommendation approaches and existing problems. Secondly, the general framework of the point of interest recommendation is proposed, which includes data sources, recommendation approaches and evaluation. Based on this framework, the various influencing factors are introduced, the current recommendation algorithms are generalized, and the evaluation metrics are summarized. Meanwhile, the representative works are analyzed, the research contents and characteristics of each type of methods are summarized in detail, and their advantages and limitations are evaluated. Finally, the challenges and potential directions for possible extensions in this filed are summarized and prospected, and the future research trends and development directions are concluded.

Key words: location-based social networks, recommendation systems, point of interest recommendation, influencing factor

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