乔 治,周 川,纪现才,曹亚男,郭 莉. 基于地域特征和异构社交关系的事件推荐算法研究[J]. 中文信息学报, 2016, 30(5): 47-56.
QIAO Zhi, ZHOU Chuan, JI Xiancai, CAO Yanan, GUO Li. Event Recommendation Based on Geographical Features and Heterogeneous Social Relationships. , 2016, 30(5): 47-56.
Event Recommendation Based on Geographical Features and Heterogeneous Social Relationships
QIAO Zhi1,2, ZHOU Chuan2,3, JI Xiancai3, CAO Yanan3, GUO Li3
1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190,China2. University of Chinese Academy of Sciences, Beijing 100049,China3. Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093,China
Abstract：In order to improve users' experience in event-based social networks (EBSNs) services, the event recommendation task has been studied in the recent years. In this paper, the user motivation data of EBSN applications is analyzed, and a novel latent factor model unifying multiple data features is proposed. This method considers two new types of features, i.e., heterogeneous online& offline social relationships and regional preference of users, and applies them for event recommendation. Experimental results on real-world data sets showed our method had better performance than some traditional methods.
[1] 彭泽环,孙乐,韩先培,石贝. 基于排序学习的微博用户推荐[J].中文信息学报, 2013, 27(4):96-102. [2] 孙建凯,王帅强,马军. Weighted-Tau Rank:一种采用加权Kendall Tau的面向排序的协同过滤算法[J].中文信息学报, 2014, 28(1): 33-40. [3] 罗成,刘奕群,张敏,马少平,茹立云,张阔. 基于用户意图识别的查询推荐研究[J].中文信息学报, 2014, 28(1): 64-72. [4] 李锐,王斌.一种基于作者建模的微博检索模型[J].中文信息学报, 2014, 28(2): 136-143. [5] Liu X, Hey Q, Tiany Y, et al. Event-based social networks linking the online and offline social worlds[C]//Proceeding of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, 2012: 1032-1040. [6] Qiao Z, Zhang P, Cao Yanan,et al. Combining Heterogeneous Social and Geographical Information for Event Recommendation[C]//Proceeding of 28th AAAI Conference on Artificial Intelligence, 2014: 145-151. [7] Salakhutdinov R, Mnih A. Probabilistic matrix factorization[C]//In Neural Information Processing Systems, 2008: 880-887. [8] Somekh O, Aizenberg N, Koren Y. Build your own music recommender by modeling internet radio streams[C]//Proceedings of the 21st international conference on World Wide Web, 2012: 1-10. [9] Wang C, Blei D M. Collaborative topic modeling for for recommending scientific articles[C]//Proceeding of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011: 448-456. [10] Zhang P, Zhou C, Wang P,et al. E-tree: An efficient indexing structure for ensemble models on data streams[J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(2): 461-474. [11] Rendle S, Freudenthaler C, Gantner Z,et al. Bpr: Bayesian personalized ranking from implicit feedback[C]//Proceeding of the 25 Conference on Uncertainty in Artificial Intelligence, 2009: 452-461. [12] Pan W, Xiang E, Yang Q. Transfer learning in collaborative filtering with uncertain ratings[C]//Proceeding of Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012: 662-668. [13] Ma H, Yang H, Lyu M R,et al. SoRec: social recommendation using probabilistic matrix factorization[C]//Proceeding of the 17th ACM conference on information and knowledge management, 2008: 931-940. [14] Liua F, and Lee H J. Use of social network information enhance collaborative filtering performance[J]. Expert Systems with Applications, 2010, 37(7): 4772-4778. [15] Xin X, King I, Deng H,et al.A social recommendation framework based on multi-scale continuous conditional random fields[C]//Proceeding of the 18th ACM conference on information and knowledge management, 2009: 1247-1256. [16] Ma H, Zhou D, Liu C, et al.Recommender systems with social regularization[C]//Proceeding of the 4 ACM international conference on Web search and data mining, 2011:287-296. [17] Lu Y, Tsaparas P, Ntoulas A,et al. Exploiting social context for review quality prediction[C]//Proceeding of the 19th International Conference on World Wide Web, 2010: 691-700. [18] Cheng C, Yang H, King I,et al. Fused matrix factorization with geographical and social influence in location-based social networks[C]//Proceeding of the 26 AAAI Conference on Artificial Intelligence, 2012: 542-548. [19] Ye M, Yin P Y, Lee W-C L,et al. Exploiting geographical influence for collaborative point-of-interest recommendation[C]//Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2011: 325-334. [20] Zheng V W, Zheng Y, Xie X, et al. Collaborative location and activity recommendations with gps history data[C]//Proceeding of the 19th international conference on World Wide Web, 2010: 1029-1038. [21] Qiao Z, Zhang P, He J,et al. Combining geographical information of users and content of items for accurate rating prediction[C]//Proceeding of 23rd International World Wide Web Conference, 2014: 361-362. [22] Takeuchi Y, Sugimoto M. Cityvoyager: An outdoor recommendation system based on user location[J]. ubiPCMM, 2005, 4(3):625-636. [23] Borzsonyil S, Kossmann D, Stocker K. The skyline operator[C]//Proceeding of 17th International Conference on Data Engineering, 2011:421-430. [24] Chaudhuri S, Gravano L. Evaluating top-k selection queries[C]//Proceeding of the 25th International Conference on Very Large Data Bases, 1999: 397-410. [25] Bruno N, Gravano L, Marian A. Evaluating top-k queries over web-accessible databases[J]. In ACM Transactions on Database Systems, 2004, 29(2): 369-369. [26] Koren Y, Bell R, Volinsky C. Matrix factorization technology for recommendation system[J]. Journal of Computer, 2009, 42(8), 30-37.
2016, Vol. 30 
