Computer Science ›› 2019, Vol. 46 ›› Issue (8): 28-34.doi: 10.11896/j.issn.1002-137X.2019.08.005
• Big Data & Data Science • Previous Articles Next Articles
DENG Cun-bin1,2, YU Hui-qun1, FAN Gui-sheng1
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[1]SUN H,HAN Z.An improved collaborative filtering algorithm for popular items of fusion items[J].Miniature Microcomputer Systems,2018,39(4):638-643.(in Chinese) 孙红,韩震.融合物品热门因子的协同过滤改进算法[J].小型微型计算机系统,2018,39(4):638-643. [2]WENG X L,WANG Z J.Research progress of collaborative filtering recommendation algorithm[J].Computer Engineering and Applications,2018,54(1):25-31.(in Chinese) 翁小兰,王志坚.协同过滤推荐算法研究进展[J].计算机工程与应用,2018,54(1):25-31. [3]XU R,ZHANG W.A recommendation system scoring prediction framework based on Adaboost algorithm[J].Journal of ComputerSystems,2017,26(8):107-113.(in Chinese) 徐日,张谧.基于Adaboost算法的推荐系统评分预测框架[J].计算机系统应用,2017,26(8):107-113. [4]PORTEOUS I,ASUNCION A,WELLING M.Bayesian matrix factorization with side information and dirichlet process mixtures [C]∥Twenty-Fourth AAAI Conference on Artificial Intelligence.AAAI Press,2010:563-568. [5]HUANG L W,JIANG B T,LU S Y,et al.A Survey of Recommendation Systems Based on Deep Learning [J].Chinese Journal of Computers,2018,41(7):191-219.(in Chinese) 黄立威,江碧涛,吕守业,等.基于深度学习的推荐系统研究综述[J].计算机学报,2018,41(7):191-219. [6]ZHU Y,LI H,LIAO Y,et al.What to do next:modeling user behaviors by time-lstm [C]∥Twenty-Sixth International Joint Conference on Artificial Intelligence.2017:3602-3608. [7]ZHENG L,NOROOZI V,YU P S.Joint deep modeling of users and items using reviews for recommendation[C]∥Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.ACM,2017:425-434. [8]CHENG H T,KOC L,HARMSEN J,et al.Wide & deep lear- ning for recommender systems[C]∥Proceedings of the 1st Workshop on Deep Learning for Recommender Systems.ACM,2016:7-10. [9]QU Y,CAI H,REN K,et al.Product-based neural networks for user response prediction[C]∥2016 IEEE 16th International Conference on Data Mining (ICDM).IEEE,2016:1149-1154. [10]HE X,LIAO L,ZHANG H,et al.Neural collaborative filtering[C]∥Proceedings of the 26th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee,2017:173-182. [11]ZHAO W,WANG W,YE J,et al.Leveraging long and short- term information in content-aware movie recommendation[J].arXiv:1712.09059,2017. [12]KIM D,PARK C,OH J,et al.Convolutional matrix factorization for document context-aware recommendation[C]∥Proceedings of the 10th ACM Conference on Recommender Systems.ACM,2016:233-240. [13]WEI J,HE J,CHEN K,et al.Collaborative filtering and deep learning based recommendation system for cold start items[J].Expert Systems with Applications,2017,69:29-39. [14]WANG H,WANG N,YEUNG D Y.Collaborative deep learning for recommender systems[C]∥Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2015:1235-1244. [15]KOREN Y.Collaborative filtering with temporal dynamics[C]∥ Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining.ACM,2009:447-456. [16]HARPER F M,KONSTAN J A.The movieLens datasets[J].Acm Transactions on Interactive Intelligent Systems,2016,5(4):1-19. [17]KAWALE J,KAWALE J,FU Y.Deep collaborative filtering via marginalized denoising auto-encoder [C]∥ACM International on Conference on Information and Knowledge Management.ACM,2015:811-820. [18]KIM D,PARK C,OH J,et al.Deep hybrid recommender systems via exploiting document context and statistics of items [J].Information Sciences,2017,417(C):72-87. |
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