基于社区发现的兴趣点推荐

doi:10.11896/jsjkx.190400440

摘要/Abstract

摘要： 近年来,LBSN(Location-based Social Networks)作为一种典型的异质信息网络越来越受到大众的关注。针对LBSN中用户签到信息十分稀疏的情况,文中提出了一种基于社区发现的兴趣点推荐算法CBR(Community-Based Recommendation)。该算法首先在社交媒体层上计算目标用户与聚类后的兴趣主题簇的相似度;其次通过兴趣主题簇与地理位置簇之间的关联矩阵R计算用户在地理位置簇上的隶属度;然后进一步融合用户的社交关系,从而得到用户对各个兴趣点的偏好分数;最后按照兴趣点的分数进行排序,以实现Top-k推荐。实验结果表明,该算法可以明显提高兴趣点的推荐质量。

关键词: 地理位置簇, 多维关系, 社区发现, 兴趣点推荐

Abstract: In recent years,LBSN (Location-based Social Networks) has attracted more and more attention as a typical heterogeneous information network.In view of the sparse check-in information of users in LBSN,this paper proposed a recommendation algorithm CBR (Community-Based Recommendation) based on community detection.It first calculates the similarity between the target user and the clustered interest topic cluster on the social media layer,and then calculates the user’s membership degree on the geographic cluster through the association matrix R between the interest topic cluster and the geographic cluster.Then it further integrates user’s social relationship to get user’s preference scores for each point of interest,and finally sorts according to the interest scores to achieve the Top-k recommendation.The experimental results show that the proposed algorithm can significantly improve the recommendation quality of the points of interest.

Key words: Community detection, Geographic cluster, Multidimensional relationship, Point-of-interest recommendation

中图分类号:

TP391

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龚卫华, 沈松. 基于社区发现的兴趣点推荐[J]. 计算机科学, 2019, 46(12): 63-68. https://doi.org/10.11896/jsjkx.190400440

GONG Wei-hua, SHEN Song. Community Detection Based Point-of-interest Recommendation[J]. Computer Science, 2019, 46(12): 63-68. https://doi.org/10.11896/jsjkx.190400440

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[1]	何亦琛, 毛宜军, 谢贤芬, 古万荣. 基于点割集图分割的矩阵变换与分解的推荐算法 Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation 计算机科学, 2022, 49(6A): 272-279. https://doi.org/10.11896/jsjkx.210600159
[2]	王本钰, 顾益军, 彭舒凡, 郑棣文. 融合动态距离和随机竞争学习的社区发现算法 Community Detection Algorithm Based on Dynamic Distance and Stochastic Competitive Learning 计算机科学, 2022, 49(5): 170-178. https://doi.org/10.11896/jsjkx.210300206
[3]	唐春阳, 肖玉芝, 赵海兴, 冶忠林, 张娜. 面向双层网络的EWCC社区发现算法 EWCC Community Discovery Algorithm for Two-Layer Network 计算机科学, 2022, 49(4): 49-55. https://doi.org/10.11896/jsjkx.210800275
[4]	杨旭华, 王磊, 叶蕾, 张端, 周艳波, 龙海霞. 基于节点相似性和网络嵌入的复杂网络社区发现算法 Complex Network Community Detection Algorithm Based on Node Similarity and Network Embedding 计算机科学, 2022, 49(3): 121-128. https://doi.org/10.11896/jsjkx.210200009
[5]	王营丽, 姜聪聪, 冯小年, 钱铁云. 时间感知的兴趣点推荐方法 Time Aware Point-of-interest Recommendation 计算机科学, 2021, 48(9): 43-49. https://doi.org/10.11896/jsjkx.210400130
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[7]	徐新黎, 肖云月, 龙海霞, 杨旭华, 毛剑飞. 基于矩阵分解的属性网络嵌入和社区发现算法 Attributed Network Embedding Based on Matrix Factorization and Community Detection 计算机科学, 2021, 48(12): 204-211. https://doi.org/10.11896/jsjkx.210300060
[8]	宁懿昕, 谢辉, 姜火文. 图神经网络社区发现研究综述 Survey of Graph Neural Network in Community Detection 计算机科学, 2021, 48(11A): 11-16. https://doi.org/10.11896/jsjkx.210500151
[9]	邢长征, 朱金侠, 孟祥福, 齐雪月, 朱尧, 张峰, 杨一鸣. 兴趣点推荐方法研究综述 Point-of-interest Recommendation:A Survey 计算机科学, 2021, 48(11A): 176-183. https://doi.org/10.11896/jsjkx.201100021
[10]	马理博, 秦小麟. 话题-位置-类别感知的兴趣点推荐 Topic-Location-Category Aware Point-of-interest Recommendation 计算机科学, 2020, 47(9): 81-87. https://doi.org/10.11896/jsjkx.191100120
[11]	张清琪, 刘漫丹. 复杂网络社区发现的多目标五行环优化算法 Multi-objective Five-elements Cycle Optimization Algorithm for Complex Network Community Discovery 计算机科学, 2020, 47(8): 284-290. https://doi.org/10.11896/jsjkx.190700082
[12]	董明刚, 弓佳明, 敬超. 基于谱聚类的多目标进化社区发现算法研究 Multi-obJective Evolutionary Algorithm Based on Community Detection Spectral Clustering 计算机科学, 2020, 47(6A): 461-466. https://doi.org/10.11896/JsJkx.191100215
[13]	张琴, 陈红梅, 封云飞. 一种基于粗糙集和密度峰值的重叠社区发现方法 Overlapping Community Detection Method Based on Rough Sets and Density Peaks 计算机科学, 2020, 47(5): 72-78. https://doi.org/10.11896/jsjkx.190400160
[14]	赵卫绩,张凤斌,刘井莲. 复杂网络社区发现研究进展 Review on Community Detection in Complex Networks 计算机科学, 2020, 47(2): 10-20. https://doi.org/10.11896/jsjkx.190100214
[15]	张敏军, 华庆一. 基于概率矩阵分解算法的社交网络用户兴趣点个性化推荐 Personalized Recommendation of Social Network Users' Interest Points Based on ProbabilityMatrix Decomposition Algorithm 计算机科学, 2020, 47(12): 144-148. https://doi.org/10.11896/jsjkx.191000064

基于社区发现的兴趣点推荐

Community Detection Based Point-of-interest Recommendation

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