计算机科学 ›› 2020, Vol. 47 ›› Issue (9): 81-87.doi: 10.11896/jsjkx.191100120
马理博, 秦小麟
MA Li-bo, QIN Xiao-lin
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。
中图分类号:
[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 21st 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. |
[1] | 骆佳磊, 孟利民. 基于路口相似度的信号配时方案推荐算法[J]. 计算机科学, 2020, 47(6A): 66-69. |
[2] | 朱磊, 胡沁涵, 赵雷, 杨季文. 基于评分偏好和项目属性的协同过滤算法[J]. 计算机科学, 2020, 47(4): 67-73. |
[3] | 赵楠, 皮文超, 许长桥. 一种面向多维特征分析过滤的视频推荐算法[J]. 计算机科学, 2020, 47(4): 103-107. |
[4] | 冯晨娇,梁吉业,宋鹏,王智强. 基于极端评分行为的相似度计算[J]. 计算机科学, 2020, 47(2): 31-36. |
[5] | 吴磊,岳峰,王含茹,王刚. 一种融合科研人员标签的学术论文推荐方法[J]. 计算机科学, 2020, 47(2): 51-57. |
[6] | 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[J]. 计算机科学, 2020, 47(12): 144-148. |
[7] | 黄超然. 基于显式反馈协同过滤算法的偏好与共性平衡[J]. 计算机科学, 2020, 47(11A): 471-473. |
[8] | 康雁, 卜荣景, 李浩, 杨兵, 张亚钏, 陈铁. 基于增强注意力机制的神经协同过滤[J]. 计算机科学, 2020, 47(10): 114-120. |
[9] | 王涵, 夏鸿斌. LDA模型和列表排序混合的协同过滤推荐算法[J]. 计算机科学, 2019, 46(9): 216-222. |
[10] | 邓存彬, 虞慧群, 范贵生. 融合动态协同过滤和深度学习的推荐算法[J]. 计算机科学, 2019, 46(8): 28-34. |
[11] | 张艳红, 张春光, 周湘贞, 王怡鸥. 项目多属性模糊联合的多样性视频推荐算法[J]. 计算机科学, 2019, 46(8): 78-83. |
[12] | 康林瑶, 唐兵, 夏艳敏, 张黎. 基于GPU加速和非负矩阵分解的并行协同过滤推荐算法[J]. 计算机科学, 2019, 46(8): 106-110. |
[13] | 刘长赟,杨宇迪,周丽华,赵丽红. 带有时间标签的流行社交位置发现[J]. 计算机科学, 2019, 46(7): 186-194. |
[14] | 王旭, 庞巍, 王喆. 异构信息网络中基于元结构的协同过滤算法[J]. 计算机科学, 2019, 46(6A): 397-401. |
[15] | 刘晴晴, 罗永龙, 汪逸飞, 郑孝遥, 陈文. 基于SVD填充的混合推荐算法[J]. 计算机科学, 2019, 46(6A): 468-472. |
|