基于位置社交网络中地点聚类推荐方法

doi:10.6040/j.issn.1672-3961.0.2015.295

摘要/Abstract

摘要： 为解决基于位置社交网络中地点推荐时遇到的数据稀疏、冷启动问题,提出一种改进的地点推荐方法,在协同过滤算法的基础上融合了聚类算法,考虑到用户偏好、朋友关系、位置语义等因素,在推荐时取两种算法的优点进行互补。研究的重点是相似度的计算,包括兴趣地点相似度、好友亲密度、词频-逆文档频率、余弦相似性。在Foursquare数据集上以准确率、召回率、单个主题的平均准确率作为度量依据,对提出的方法进行验证。试验证明,本方法有效提高了推荐效果。

关键词: 位置推荐, 聚类, 协同过滤, 基于位置的社交网络

Abstract: In order to solve the data sparse and cold start in spot recommendation in the location-based social networking, an improved spot recommendation method was proposed. Based on the clustering algorithm and the collaborative filtering algorithm, the user preferences, friend relations, semantic location and other factors was taken into account. The advantages of the two methods were complemented. The focus of this research was the calculation of similarity, which included location similarity, friends intimacy measure, term frequency inverse document frequency, cosine similarity.To verify the proposed methods, precision, recall,mean average precision was used as a measure on Foursquare dataset. The results showed that the proposed method could effectively improve the recommendation effect.

Key words: clustering, collaborative filtering, location-based social network, spot recommendation

中图分类号:

TP391

李朔,石宇良. 基于位置社交网络中地点聚类推荐方法[J]. 山东大学学报(工学版), 2016, 46(3): 44-50.

LI Shuo, SHI Yuliang. The method of spot cluster recommendation in location-based social networks[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(3): 44-50.

参考文献

[1] 朱立超,李治军,姜守旭.基于位置的社交网络研究综述[J].智能计算机与应用,2014,4(4):60-67. ZHU Lichao, LI Zhijun, JIANG Shouxu. An overview of location based social network[J].Intelligent Computer and Applications, 2014, 4(4):60-67.
[2] 吴昊,刘东苏.社交网络中的好友推荐方法研究[J].现代图书情报技术,2015(1):59-65. WU Hao, LIU Dongsu. Friend recommendation in social network[J].New Technology of Library and Information Service, 2015(1):59-65.
[3] ZHENG Y, ZHANG L, XIE X, et al.Mining interesting locations and travel sequences from GPS trajectories[C] //Proceedings of the 18th International Conference on World Wide Web. New York, USA:ACM, 2009:791-800.
[4] BAO J, ZHENG Y, MOKBEL F M.Location-based and preference-aware recommendation using sparse geo-social networking data[C] // Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in GIS. New York, USA:ACM, 2012:199-208.
[5] ZHENG V W, ZHENG Y, XIE X, et al. Collaborative location and activityrecommendations with gps history data[C] //Proceedings of the 19th International Conference on World Wide Web. New York, USA: ACM, 2010:1029-1038.
[6] 翟红生,于海鹏.在线社交网络中的位置服务研究进展与趋势[J].计算机应用研究,2013, 11(30):3223-3227. ZHAI Hongsheng, YU Haipeng. Present situation and trend of research of location-based service on online social networks[J].Application Research of Computers, 2013, 11(30):3223-3227.
[7] 朱立超.基于位置的社交网络中个性化路径推荐算法的研究[D].哈尔滨:哈尔滨工业大学,2014. ZHU Lichao. LBSN based personalized routes recommendation[D].Harbin: Harbin Institute of Technology, 2014.
[8] 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. Beijing, China:ACM, 2011:325-334.
[9] YING J C, LU H C, KOU W N, et al. Urban point-of-interest recommendation by mining user check-in behaviors[C] // Proceedings of the ACM SIGKDD International Workshop on Urban Computing. Beijing, China:ACM, 2012:63-70.
[10] 朱荣鑫.基于地理位置的社交网络潜在用户和位置推荐模型研究[D].南京:南京邮电大学,2013. ZHU Rongxin. Research on the model of latent user and location recommendation in location-based social networks[D].Nanjing: Nanjing University of Posts, 2013.
[11] 任克江.基于地理信息的检索和用户数据挖掘[D].大连:大连理工大学,2013. REN Kejiang. Information retrieval and user data mining based on geographic information[D].Dalian:Dalian University of Technology, 2013.
[12] CHO E, MYERS S A, LESKOVEC J. Friendship and mobility: user movement in location-basedsocial networks[C] // Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. California, USA: ACM, 2011:1082-1090.
[13] 王立军.基于协同过滤推荐系统的数据稀疏性问题研究[D].长春:东北师范大学,2009. WANG Lijun. Research on data sparsity problem of collaborative filtering recommendation system[D]. Changchun: Northeast Normal University, 2009.
[14] SUN Dongting, HE Tao, ZHANG Fuhai. Survey of cold-start problem in collaborative filtering recommender system[J].Computer and Modernization, 2012, 1(201):59-63.
[15] FRENCE G, YE M, LEE W C. Location recommendation for out-of-town users inlocation-based social networks[C] //Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. San Francisco, USA:ACM, 2013: 721-726.

相关文章 15

[1]	李晓辉,刘小飞,孙炜桐,赵毅,董媛,靳引利. 基于车辆与无人机协同的巡检任务分配与路径规划算法[J]. 山东大学学报 (工学版), 2025, 55(5): 101-109.
[2]	陈素根,赵志忠. 融合局部截断距离及小簇合并的密度峰值聚类[J]. 山东大学学报 (工学版), 2025, 55(2): 58-70.
[3]	王梅,宋凯文,刘勇,王志宝,万达. DMKK-means——一种深度多核K-means聚类算法[J]. 山东大学学报 (工学版), 2024, 54(6): 1-7.
[4]	王丽娟,徐晓,丁世飞. 面向密度峰值聚类的高效相似度度量[J]. 山东大学学报 (工学版), 2024, 54(3): 12-21.
[5]	张鑫,费可可. 基于log鲁棒核岭回归的子空间聚类算法[J]. 山东大学学报 (工学版), 2023, 53(6): 26-34.
[6]	李兆彬,叶军,周浩岩,卢岚,谢立. 变异萤火虫优化的粗糙K-均值聚类算法[J]. 山东大学学报 (工学版), 2023, 53(4): 74-82.
[7]	侯延琛,赵金东. 任意形状聚类的SPK-means算法[J]. 山东大学学报 (工学版), 2023, 53(2): 87-92.
[8]	程业超,刘惊雷. 自适应图正则的单步子空间聚类[J]. 山东大学学报 (工学版), 2022, 52(2): 57-66.
[9]	卢建云,张蔚,李林. 一种基于动态局部密度和聚类结构的聚类算法[J]. 山东大学学报 (工学版), 2022, 52(2): 118-127.
[10]	孟银凤,杨佳宇,曹付元. 函数型数据的分裂转移式层次聚类算法[J]. 山东大学学报 (工学版), 2022, 52(1): 19-27.
[11]	朱恒东, 马盈仓, 代雪珍. 自适应半监督邻域聚类算法[J]. 山东大学学报 (工学版), 2021, 51(4): 24-34.
[12]	朱昌明,岳闻,王盼红,沈震宇,周日贵. 主动三支聚类下的全局和局部多视角多标签学习算法[J]. 山东大学学报 (工学版), 2021, 51(2): 34-46.
[13]	解子奇,王立宏,李嫚. 块对角子空间聚类中成对约束的主动式学习[J]. 山东大学学报 (工学版), 2021, 51(2): 65-73.
[14]	李蓓,赵松,谢志佳,牛萌. 电动汽车虚拟储能可用容量建模[J]. 山东大学学报 (工学版), 2020, 50(6): 101-111.
[15]	张胜男,王雷,常春红,郝本利. 基于三维剪切波变换和BM4D的图像去噪方法[J]. 山东大学学报 (工学版), 2020, 50(2): 83-90.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed