融合时空上下文信息的兴趣点推荐  

Point-of-Interest Recommendation with Spatio-Temporal Context Awareness

作　　者：徐前方[1] 王嘉春 肖波[1,2] XU Qian-fang1, WANG Jia-chun1 , Jia-chun , XIAO Bo1,2(1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China ; 2. Institute of Sensing Technology and Business, Beijing University of Posts and Telecommunications, Jiangsu Wuxi 214135, Chin)

机构地区：[1]北京邮电大学信息与通信工程学院,北京100876 [2]无锡北邮感知技术产业研究院有限公司,江苏无锡214135

出　　处：《北京邮电大学学报》2018年第1期37-42,50,共7页Journal of Beijing University of Posts and Telecommunications

摘　　要：为了给用户提供更好的位置服务,提出了一种位置社交网络中融入时空上下文信息的混合个性化兴趣点推荐模型.在空间上,对用户签到进行层次聚类,对各聚类内二维核密度估计的结果取平均.在时间上,利用用户签到的时间信息、签到的位置信息及社交网络构建转移矩阵,运行改进图的随机游走模型.混合模型融合时空上下文信息做推荐.在真实数据集上的实验结果表明,无论在标准推荐场景还是冷启动场景下,混合推荐模型的准确率和召回率性能均优于基准方法.A personalized hybrid point-of-interest recommendation with spatio-temporal context awareness was proposed to provide users in location-based social networks with superior service. Spatially,two-dimension kernel density estimation was performed for each cluster of check-ins derived by hierarchical clustering and averaged. Meanwhile,random walk on graph was iterated on transition matrices generated from sequence information,location information and social network. The hybrid model combines spatiotemporal context above for recommendation. Experiment on real-world location-based social network（ LBSN） datasets demonstrates that the performance metrics of precision and recall of the hybrid recommendation model is superior to other baseline techniques in both standard recommendation scene and cold-start scene.

关 键 词：位置社交网络 时空上下文 兴趣点推荐 图的随机游走 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]