计算机科学 ›› 2020, Vol. 47 ›› Issue (9): 81-87.doi: 10.11896/jsjkx.191100120

• 数据库&大数据&数据科学 • 上一篇    下一篇

话题-位置-类别感知的兴趣点推荐

马理博, 秦小麟   

  1. 南京航空航天大学计算机科学与技术学院 南京210016
  • 收稿日期:2019-11-15 发布日期:2020-09-10
  • 通讯作者: 秦小麟(qinxcs@nuaa.edu.cn)
  • 作者简介:mlbcs@nuaa.edu.cn
  • 基金资助:
    国家自然科学基金项目(61373015,61728204)

Topic-Location-Category Aware Point-of-interest Recommendation

MA Li-bo, QIN Xiao-lin   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • Received:2019-11-15 Published:2020-09-10
  • About author:MA Li-bo,born in 1997,postgraduate.His main research interests include data management and recommendation system.
    QIN Xiao-lin,born in 1953,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include spatio-temporal database,distributed data management and security,etc.
  • Supported by:
    National Natural Science Foundation of China (61373015,61728204).

摘要: 随着基于位置的社交网络(Location-Based Social Networks,LBSN)的不断发展,有助于用户探索新地点和商家发现潜在客户的兴趣点(Point-of-Interest,POI)推荐受到了广泛关注。然而,用户签到数据的高稀疏性,为兴趣点推荐带来了严峻挑战。针对这一挑战,文中探索兴趣点的文本、地理和类别信息,有效融合兴趣话题、地理影响及类别偏好因素,提出了一种话题-位置-类别感知的协同过滤兴趣点推荐算法,称之为TGC-CF。该算法利用潜在狄利克雷分配(Latent Dirichlet Allocation,LDA)模型挖掘兴趣点相关的文本信息,学习用户的兴趣话题分布,并计算用户间兴趣话题分布的相似度,通过结合地理距离和用户的区域偏好来建模地理影响;使用TF-IDF统计方法评估目标用户对类别的偏好程度,并考虑其他用户的类别偏好在推荐过程中的作用和影响,最后将这些影响因素整合到一个协同过滤推荐模型中,从而生成包含用户感兴趣的兴趣点的推荐列表。在两个真实数据集上的实验结果表明,TGC-CF算法比其他推荐算法表现更好。

关键词: 基于位置的社交网络, 兴趣点推荐, 话题模型, 地理影响, 协同过滤

Abstract: With the continuous development of Location-Based Social Networks(LBSN),Point-of-Interest(POI) recommendations that help users explore new locations and merchants discover potential customers has received widespread attention.How-ever,due to the high sparsity of the users’ check-in data,POI recommendation faces serious challenges.To cope with this challenge,this paper explores the textual information,geographic information,and category information,incorporating interest topics,geographical influence,and category preference factors effectively,and proposes a topic-location-category aware collaborative filtering algorithm called TGC-CF for POI recommendation.The proposed algorithm uses the Latent Dirichlet Allocation(LDA) model to learn the interest topics distribution of users and calculate the similarity of interest topics distribution among users by mining textual information associated with POIs,models geographical influence by combining geographic distance and user’s regionalpre-ference,uses the TF-IDF statistical method to assess the target user’s preference for the category and consider the impact of other users’ category preference in the recommendation process,and finally integrate these influencing factors into a collaborative filtering recommendation model to generate a list of recommendations containing POIs that users are interested in.Experimental results on two real data sets show that TGC-CF algorithm performs better than other recommendation algorithms.

Key words: Location-based social networks, POI recommendation, Topic model, Geographical influence, Collaborative filtering

中图分类号: 

  • TP311
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