[1] ADOMAVICIUS G, TUZHILIN A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749. [2] 王岩, 张杰, 许合利.结合用户兴趣和改进的协同过滤推荐算法[J]. 小型微型计算机系统, 2020, 41(8): 665-1669. WANG Y, ZHANG J, XU H L.Combining user interests with improved collaborative filtering recommendation algorithm[J]. Journal of Chinese Computer Systems, 2020, 41(8): 1665-1669.(in Chinese) [3] LI Y, WANG H J, LIU H L, et al. A study on content-based video recommendation[C]//Proceedings of 2017 IEEE International Conference on Image Processing.Washington D.C., USA:IEEE Press, 2017:4581-4585. [4] YANG J R, YANG C, HU X W.A study of hybrid recommendation algorithm based on user[C]//Proceedings of International Conference on Intelligent Human-Machine Systems & Cybernetics.Washington D.C., USA:IEEE Press, 2016:261-264. [5] ELKAHKY A M, SONG Y, He X.A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//Proceedings of the 24th International Conference on World Wide Web.New York, USA:ACM Press, 2015:278-288. [6] HE X N, CHUA T S.Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR Conference.New York, USA:ACM Press, 2017:355-364. [7] ZHENG L, NOROOZI V, YU P S.Joint deep modeling of users and items using reviews for recommendation[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining.New York, USA:ACM Press, 2017:425-434. [8] XUE H J, DAI X Y, ZHANG J B, et al. Deep matrix factorization models for recommender systems[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.California, USA:AAAI Press, 2017:3203-3209. [9] CHEN J Y, ZHANG H W, HE X G, et al. Attentive collaborative filtering:multimedia recommendation with item-and component-level attention[C]//Proceedings of the 40th International ACM SIGIR Conference.New York, USA:ACM Press, 2017:335-344. [10] MNIH V, HEESS N, GRAVES A, et al. Recurrent models of visual attention[C]//Proceedings of NIPS'11.Cambridge.USA:MIT Press, 2014:2204-2212. [11] XIA B, LI Y, LI Q M, et al. Attention-based recurrent neural network for location recommendation[C]//Proceedings of International Conference on Intelligent Systems and Knowledge Engineering.Washington D.C., USA:IEEE Press, 2017:1-6. [12] CHEN J W, ZHUANG F Z, XIN H, et al. Attention-driven factor model for explainable personalized recommendation[C]//Proceedings of the 41st International ACM SIGIR Conference.New York, USA:ACM Press, 2018:909-912. [13] SOTTOCORNOLA G, STELLA F, ZANKER M, et al. Towards a deep learning model for hybrid recommendation[C]//Proceedings of the International Conference on Web Intelligence.New York, USA:ACM Press, 2017:1260-1264. [14] 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): 1619-1647. HUANG L W, JIANG B T, LÜ S Y, et al. Deep learning based recommender systems[J]. Chinese Journal of Computers, 2018, 41(7): 1619-1647.(in Chinese) [15] 刘凯, 张立民, 周立军.深度学习在信息推荐系统的应用综述[J]. 小型微型计算机系统, 2019, 40(4): 52-57. LIU K, ZHANG L M, ZHOU L J.Survey of deep learning applied in information recommendation system[J]. Journal of Chinese Computer Systems, 2019, 40(4): 52-57. [16] HE X N, LIAO L Z, ZHANG H W, et al. Neural collaborative filtering[C]//Proceedings of the 26th International World Wide Web Conference.Berlin, Germany:Springer, 2017:173-182. [17] HARPER F M, KONSTAN J A.The movielens datasets:history and context[J]. ACM Transactions on Interactive Intelligent Systems, 2015, 5(4): 1-19. [18] 陈晓霞, 卢菁.融合多数据源的动态自适应推荐算法[J]. 计算机工程, 2018, 44(9): 64-69. CHEN X X, LU J.Dynamic adaptive recommendation algorithm fusing multiple data sources[J]. Computer Engineering, 2018, 44(9): 64-69.(in Chinese) [19] 钱晓捷, 张路一.融合评分结构特征与偏好距离的协同过滤推荐算法[J]. 计算机工程, 2017, 43(5): 185-190, 196. QIAN X J, ZHANG L Y.Collaborative filtering recommendation algorithm on integration of grade structure feature and preference distance[J]. Computer Engineering, 2017, 43(5): 185-190, 196.(in Chinese) [20] JABAKJI A, DAG H.Improving item-based recommendation accuracy with user's preferences on apache mahout[C]//Proceedings of IEEE International Conference on Big Data.Washington D.C., USA:IEEE Press, 2016:1742-1749. [21] WANG H, WANG N Y, YEUNG D-Y.Collaborative deep learning for recommender systems[C]//Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'15).Association for Computing Machinery.New York, USA:ACM Press, 2015:1235-1244. [22] HE X N, HE Z K, SONG J K, et al. NAIS:neural attentive item similarity model for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(12): 2354-2366. |