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·524· 智能系统学报 第14卷 ontology and sequential pattern mining[J].Future genera- [16]JARVELIN K,KEKALAINEN K.Cumulated gain-based tion computer systems,2017,72:37-48. evaluation of IR techniques[J].ACM transactions on in- [8]KOREN Y.BELL R.VOLINSKY C.Matrix factorization formation systems (TOIS),2002,20(4):422-446. techniques for recommender systems[J].Computer,2009, [17]LINDEN G.SMITH B,YORK J.Amazon.com recom- 42(8):30-37. mendations:item-to-item collaborative filtering[J].IEEE [9]MA Hao,ZHOU Dengyong,LIU Chao,et al.Recommend- internet computing,2003,7(1):76-80. er systems with social regularization[C]//Proceedings of [18]SYMEONIDIS P.Content-based dimensionality reduc- the 4th ACM International Conference on Web Search and tion for recommender systems[M]//PREISACH C, Data Mining.Hong Kong,China:ACM,2011:287-296. BURKHARDT H,SCHMIDT-THIEME L,et al.Data [10]ANSARI A,LI Yang,ZHANG J Z.Probabilistic topic Analysis,Machine Learning and Applications.Berlin, model for hybrid recommender systems:a stochastic vari- Heidelberg:Springer,2008:619-626. ational Bayesian approach[D].New York:Columbia [19]ZHAO Xiangyu,NIU Zhendong,CHEN Wei,et al.A hy- Business School,2017. brid approach of topic model and matrix factorization [11]彭敏,席俊杰,代心媛,等.基于情感分析和LDA主题 based on two-step recommendation framework[J].Journ- 模型的协同过滤推荐算法[】.中文信息学报,2017, al of intelligent information systems,2015,44(3):335- 31(2):194203 353. PENG Min,XI Junjie,DAI Xinyuan,et al.Collaborative 作者简介: filtering recommendation based on sentiment analysis and 张旭,男,1991年生,硕土研究 LDA topic model[J].Journal of Chinese information pro- 生,主要研究方向为智能信息处理、推 cessing,2017,31(2):194-203. 荐系统。 [12]黄璐,林川杰,何军,等.融合主题模型和协同过滤的多 样化移动应用推荐.软件学报,2017,28(3):708-720. HUANG Lu,LIN Chuanjie,HE Jun,et al.Diversified mobile app recommendation combining topic model and collaborative filtering[J].Journal of software,2017,28(3): 孙福振,男,1978年生,副教授, 708-720. 博士,主要研究方向为信息检索与数 [13]BLEI D M.NG A Y.JORDAN M I,et al.Latent dirich- 据挖掘、推荐系统、话题检测与热点跟 踪。授权国家发明专利6项。发表学 let allocation[J].Journal of machine learning research, 术论文30余篇。 2003,3(4/5):993-1022. [14]GRIFFITHS TL,STEYVERS M.Finding scientific top- ics[J].Proceedings of the national academy of sciences of the United States of America,2004,101(S1):5228-5235 方春,女,1981年生,讲师,博士, [15]SHI Yue,KARATZOGLOU A,BALTRUNAS L,et al. 主要研究方向为智能计算、模式识别、 生物医学研究。发表学术论文10余篇。 CLiMF:learning to maximize reciprocal rank with collab- orative less-is-more filtering[C]//Proceedings of the 6th ACM conference on recommender systems.Dublin,Ire- land:ACM2012:139-146.ontology and sequential pattern mining[J]. Future genera￾tion computer systems, 2017, 72: 37–48. KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30–37. [8] MA Hao, ZHOU Dengyong, LIU Chao, et al. Recommend￾er systems with social regularization[C]//Proceedings of the 4th ACM International Conference on Web Search and Data Mining. Hong Kong, China: ACM, 2011: 287–296. [9] ANSARI A, LI Yang, ZHANG J Z. Probabilistic topic model for hybrid recommender systems: a stochastic vari￾ational Bayesian approach[D]. New York: Columbia Business School, 2017. [10] 彭敏, 席俊杰, 代心媛, 等. 基于情感分析和 LDA 主题 模型的协同过滤推荐算法[J]. 中文信息学报, 2017, 31(2): 194–203. PENG Min, XI Junjie, DAI Xinyuan, et al. Collaborative filtering recommendation based on sentiment analysis and LDA topic model[J]. Journal of Chinese information pro￾cessing, 2017, 31(2): 194–203. [11] 黄璐, 林川杰, 何军, 等. 融合主题模型和协同过滤的多 样化移动应用推荐[J]. 软件学报, 2017, 28(3): 708–720. HUANG Lu, LIN Chuanjie, HE Jun, et al. Diversified mobile app recommendation combining topic model and collaborative filtering[J]. Journal of software, 2017, 28(3): 708–720. [12] BLEI D M, NG A Y, JORDAN M I, et al. Latent dirich￾let allocation[J]. Journal of machine learning research, 2003, 3(4/5): 993–1022. [13] GRIFFITHS T L, STEYVERS M. Finding scientific top￾ics[J]. Proceedings of the national academy of sciences of the United States of America, 2004, 101(S1): 5228–5235. [14] SHI Yue, KARATZOGLOU A, BALTRUNAS L, et al. CLiMF: learning to maximize reciprocal rank with collab￾orative less-is-more filtering[C]//Proceedings of the 6th ACM conference on recommender systems. Dublin, Ire￾land: ACM, 2012: 139–146. [15] JÄRVELIN K, KEKÄLÄINEN K. Cumulated gain-based evaluation of IR techniques[J]. ACM transactions on in￾formation systems (TOIS), 2002, 20(4): 422–446. [16] LINDEN G, SMITH B, YORK J. Amazon. com recom￾mendations: item-to-item collaborative filtering[J]. IEEE internet computing, 2003, 7(1): 76–80. [17] SYMEONIDIS P. Content-based dimensionality reduc￾tion for recommender systems[M]//PREISACH C, BURKHARDT H, SCHMIDT-THIEME L, et al. Data Analysis, Machine Learning and Applications. Berlin, Heidelberg: Springer, 2008: 619–626. [18] ZHAO Xiangyu, NIU Zhendong, CHEN Wei, et al. A hy￾brid approach of topic model and matrix factorization based on two-step recommendation framework[J]. Journ￾al of intelligent information systems, 2015, 44(3): 335– 353. [19] 作者简介: 张旭,男,1991 年生,硕士研究 生,主要研究方向为智能信息处理、推 荐系统。 孙福振,男,1978 年生,副教授, 博士,主要研究方向为信息检索与数 据挖掘、推荐系统、话题检测与热点跟 踪。授权国家发明专利 6 项。发表学 术论文 30 余篇。 方春,女,1981 年生,讲师,博士, 主要研究方向为智能计算、模式识别、 生物医学研究。发表学术论文 10 余篇。 ·524· 智 能 系 统 学 报 第 14 卷
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