话题-位置-类别感知的兴趣点推荐

doi:10.11896/jsjkx.191100120

摘要/Abstract

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: 地理影响, 话题模型, 基于位置的社交网络, 协同过滤, 兴趣点推荐

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

中图分类号:

TP311

马理博, 秦小麟. 话题-位置-类别感知的兴趣点推荐[J]. 计算机科学, 2020, 47(9): 81-87. https://doi.org/10.11896/jsjkx.191100120

MA Li-bo, QIN Xiao-lin. Topic-Location-Category Aware Point-of-interest Recommendation[J]. Computer Science, 2020, 47(9): 81-87. https://doi.org/10.11896/jsjkx.191100120

参考文献

[1] SASSI I B,MELLOULI S,YAHIA S B.Context-aware recommender systems in mobile environment:On the road of future research[J].Information Systems,2017,72:27-61.
[2] WANG H,FU Y,WANG Q,et al.A location-sentiment-aware recommender system for both home-town and out-of-town users[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2017:1135-1143.
[3] TOBLER W R.A Computer Movie Simulating Urban Growth in the Detroit Region[J].Economic Geography,1970,46(Supp 1):234-240.
[4] STEPAN T,MORAWSKI J M,DICK S,et al.Incorporatingspatial,temporal,and social context in recommendations for location-based social networks[J].IEEE Trans.Comput Soc.Syst.,2016,3(4):164-175.
[5] WU H,SHAO J,YIN H,et al.Geographical Constraint andTemporal Similarity Modeling for Point-of-Interest Recommendation[C]//International Conference on Web Information Systems Engineering.2015:426-441.
[6] LIAN D,ZHAO C,XIE X,et al.Geomf:joint geographical modeling and matrix factorization for point-of-interest recommendation[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2014:831-840.
[7] XING S,LIU F,WANG Q,et al.Content-aware point-of-interest recommendation based on convolutional neural network[J].Appl.Intell.,2019,49:858.
[8] REN X Y,SONG M N,SONG J D.Context-aware Point-of-interest Recommendation in Location-Based Social Networks[J].Chinese Journal of Computers,2017,40(4):824-841.
[9] ZHU Z Q,CAO J X,WENG C H.Location-time-sociality aware personalized tourist attraction recommendation in LBSN[C]//IEEE 22nd International Conference on Computer Supported Cooperative Work in Design.2018:636-641.
[10] WANG H,TERROVITIS M,MAMOULIS N.Location recommendationin location-based social networks using user check-in data[C]//Proceedings of the 21^st ACM SIGS PATIAL International Conference on Advances in Geographic Information Systems.2013:374-383.
[11] YUAN Q,CONG G,MA Z,et al.Time aware point-of-interest recommendation[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval.2013:363-372.
[12] SI Y L,ZHANG F Z,LIU W Y.An adaptive point-of-interest recommendation method for location-based social networks based on user activity and spatial features[J].Knowledge-Based Systems,2019,163:267-282.
[13] BAO J,ZHENG Y,MOKBEL M F.Location-based and prefe-rence-aware recommendation using sparse geo-social networking data[C]//Proceedings of the 20th International Conference on Advances in Geographic Information Systems.2012:199-208.
[14] XIAN X F,CHEN X J,ZHAO P P,et al.Next point of interest recommendation based on context awareness and personalized metrics[J].Computer Engineering and Science,2018,40(4):616-625.
[15] YE M,YIN P,LEE W C,et al.Exploiting geographical influence for collaborative point-of-interest recommendation[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.2011:325-334.
[16] CHENG C,YANG H,KING I,et al.Fused matrix factorization with geographical and social influence in location-based social networks[C]//Twenty-Sixth AAAI Conference on Artificial Intelligence.2012:17-23.
[17] LIU B,FU Y,YAO Z,et al.Learning geographical preferences for point-of-interest recommendation[C]//19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2013:1043-1051.
[18] ZHANG J D,CHOW C Y.GeoSoCa:exploiting geographical,social and categorical correlations for point-of-interest recommendations[C]//38th International ACM SIGIR Conference on Research and Development in Information Retrieval.2015:443-452.
[19] YANG D Q,ZHANG D Q,ZHENG V W,et al.Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2015,45(1):129-142.
[20] HU L K,SUN A X,LIU Y.Your neighbors affect your ratings:on geographical neighborhood influence to rating prediction[C]//Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval.2014:345-354.

相关文章 15

[1]	程章桃, 钟婷, 张晟铭, 周帆. 基于图学习的推荐系统研究综述 Survey of Recommender Systems Based on Graph Learning 计算机科学, 2022, 49(9): 1-13. https://doi.org/10.11896/jsjkx.210900072
[2]	王冠宇, 钟婷, 冯宇, 周帆. 基于矢量量化编码的协同过滤推荐方法 Collaborative Filtering Recommendation Method Based on Vector Quantization Coding 计算机科学, 2022, 49(9): 48-54. https://doi.org/10.11896/jsjkx.210700109
[3]	孙晓寒, 张莉. 基于评分区域子空间的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace 计算机科学, 2022, 49(7): 50-56. https://doi.org/10.11896/jsjkx.210600062
[4]	蔡晓娟, 谭文安. 一种改进的融合相似度和信任度的协同过滤算法 Improved Collaborative Filtering Algorithm Combining Similarity and Trust 计算机科学, 2022, 49(6A): 238-241. https://doi.org/10.11896/jsjkx.210400088
[5]	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
[6]	郭亮, 杨兴耀, 于炯, 韩晨, 黄仲浩. 基于注意力机制和门控网络相结合的混合推荐系统 Hybrid Recommender System Based on Attention Mechanisms and Gating Network 计算机科学, 2022, 49(6): 158-164. https://doi.org/10.11896/jsjkx.210500013
[7]	董晓梅, 王蕊, 邹欣开. 面向推荐应用的差分隐私方案综述 Survey on Privacy Protection Solutions for Recommended Applications 计算机科学, 2021, 48(9): 21-35. https://doi.org/10.11896/jsjkx.201100083
[8]	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
[9]	詹皖江, 洪植林, 方路平, 吴哲夫, 吕跃华. 基于对抗性学习的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning 计算机科学, 2021, 48(7): 172-177. https://doi.org/10.11896/jsjkx.200600077
[10]	邵超, 宋淑米. 基于信任关系下用户兴趣偏好的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on User Preference Under Trust Relationship 计算机科学, 2021, 48(6A): 240-245. https://doi.org/10.11896/jsjkx.200700113
[11]	武建新, 张志鸿. 融合用户评分与显隐兴趣相似度的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on User Rating and Similarity of Explicit and Implicit Interest 计算机科学, 2021, 48(5): 147-154. https://doi.org/10.11896/jsjkx.200300072
[12]	肖诗涛, 邵蓥侠, 宋卫平, 崔斌. 面向协同过滤推荐的新型混合评分函数 Hybrid Score Function for Collaborative Filtering Recommendation 计算机科学, 2021, 48(3): 113-118. https://doi.org/10.11896/jsjkx.200900067
[13]	郝志峰, 廖祥财, 温雯, 蔡瑞初. 基于多上下文信息的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Multi-context Information 计算机科学, 2021, 48(3): 168-173. https://doi.org/10.11896/jsjkx.200700101
[14]	韩立锋, 陈莉. 融合用户属性与项目流行度的用户冷启动推荐模型 User Cold Start Recommendation Model Integrating User Attributes and Item Popularity 计算机科学, 2021, 48(2): 114-120. https://doi.org/10.11896/jsjkx.200900152
[15]	李康林, 古天龙, 宾辰忠. 多空间交互协同过滤推荐 Multi-space Interactive Collaborative Filtering Recommendation 计算机科学, 2021, 48(12): 181-187. https://doi.org/10.11896/jsjkx.201100031

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed