[1]张旭,孙福振,方春.加权高效用因子的两阶段混合推荐算法[J].智能系统学报,2019,14(3):518-524.[doi:10.11992/tis.201710028]
 ZHANG Xu,SUN Fuzhen,FANG Chun.Two-phase weighted high-utility factor-based hybrid recommendation algorithm[J].CAAI Transactions on Intelligent Systems,2019,14(3):518-524.[doi:10.11992/tis.201710028]
点击复制

加权高效用因子的两阶段混合推荐算法

参考文献/References:
[1] WEIMER M, KARATZOGLOU A, LE Q V, et al. COFIRANK maximum margin matrix factorization for collaborative ranking[C]//Proceedings of the 20th International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada:Curran Associates Inc., 2007:1593-1600.
[2] LIU N N, ZHAO Min, YANG Qiang. Probabilistic latent preference analysis for collaborative filtering[C]//Proceedings of the 18th ACM Conference on Information and Knowledge Management. Hong Kong, China:ACM, 2009:759-766.
[3] BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C]//Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Madison, Wisconsin:Morgan Kaufmann Publishers Inc., 1998:43-52.
[4] LI Sheng, KAWALE J, FU Yun. Deep collaborative filtering via marginalized denoising auto-encoder[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne, Australia:ACM, 2015:811-820.
[5] KA??áK O, KOMPAN M, BIELIKOVá M. Personalized hybrid recommendation for group of users:Top-N multimedia recommender[J]. Information processing and management, 2016, 52(3):459-477.
[6] LI Sheng, KAWALE J, FU Yun. Predicting user behavior in display advertising via dynamic collective matrix factorization[C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information. Santiago, Chile:ACM, 2015:875-878.
[7] TARUS J K, NIU Zhendong, YOUSIF A. A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining[J]. Future generation computer systems, 2017, 72:37-48.
[8] KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8):30-37.
[9] MA Hao, ZHOU Dengyong, LIU Chao, et al. Recommender 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.
[10] ANSARI A, LI Yang, ZHANG J Z. Probabilistic topic model for hybrid recommender systems:a stochastic variational Bayesian approach[D]. New York:Columbia Business School, 2017.
[11] 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 processing, 2017, 31(2):194-203
[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[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
[13] BLEI D M, NG A Y, JORDAN M I, et al. Latent dirichlet allocation[J]. Journal of machine learning research, 2003, 3(4/5):993-1022.
[14] GRIFFITHS T L, STEYVERS M. Finding scientific topics[J]. Proceedings of the national academy of sciences of the United States of America, 2004, 101(S1):5228-5235.
[15] SHI Yue, KARATZOGLOU A, BALTRUNAS L, et al. CLiMF:learning to maximize reciprocal rank with collaborative less-is-more filtering[C]//Proceedings of the 6th ACM conference on recommender systems. Dublin, Ireland:ACM, 2012:139-146.
[16] J?RVELIN K, KEK?L?INEN K. Cumulated gain-based evaluation of IR techniques[J]. ACM transactions on information systems (TOIS), 2002, 20(4):422-446.
[17] LINDEN G, SMITH B, YORK J. Amazon. com recommendations:item-to-item collaborative filtering[J]. IEEE internet computing, 2003, 7(1):76-80.
[18] SYMEONIDIS P. Content-based dimensionality reduction 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.
[19] ZHAO Xiangyu, NIU Zhendong, CHEN Wei, et al. A hybrid approach of topic model and matrix factorization based on two-step recommendation framework[J]. Journal of intelligent information systems, 2015, 44(3):335-353.
相似文献/References:
[1]王晓林,苏松志,刘晓颖,等.一种基于级联神经网络的飞机检测方法[J].智能系统学报,2020,15(4):697.[doi:10.11992/tis.201908028]
 WANG Xiaolin,SU Songzhi,LIU Xiaoying,et al.Cascade convolutional neural networks for airplane detection[J].CAAI Transactions on Intelligent Systems,2020,15(3):697.[doi:10.11992/tis.201908028]

备注/Memo

收稿日期:2017-10-31。
基金项目:国家自然科学基金项目(61602280);山东省自然科学基金项目(ZR2014FQ028).
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。
通讯作者:孙福振.E-mail:sunfuzhen@sdut.edu.cn

更新日期/Last Update: 1900-01-01
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134