Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 415-422.
• Big Date & Date Mining • Previous Articles Next Articles
HE Ming, YAO Kai-sheng,YANG Peng,ZHANG Jiu-ling
CLC Number:
| [1]ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[C]∥Proceedings of the IEEE Transactions Knowledge and Data Engineering,2005,17(6):734-749. [2]KATARYA R,VERMA O P.Privacy-Preserving and Secure Recommender System Enhance with K-NN and Social Tagging[C]∥2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud).New York,2017:52-57. [3]HUANG L W,LI D Y.A review of information recommendation in social media[J].CAAI Trans.on Intelligent Systems,2012,7(1):1-8. [4]SU X,KHOSHGOFTAAR T M.A survey of collaborative filtering techniques [J].Advances in Artificial Intelligence,2009,2009(12):4. [5]ORTEGA F,HERNANDO A,BOBADILLA J,et al.Recom- mending items to group of users using Matrix Factorization based Collaborative Filtering[J].Information Sciences,2016,345(C):313-324. [6]LIN J,SUGIYAMA K,KAN M Y,et al.Addressing cold-start in app recommendation:latent user models constructed from twitter followers[C]∥36th International ACM SIGIR Conference on Research and Development in Information Retrieval.Dublin,Ireland,2013:283-292. [7]WANG L C,MENG X W,ZHANG Y J.Context-Aware recommender systems:A survey of the state-of-the-art and possible extensions[J].Journal of Software,2012,23(1):1-20. [8]SYMEONIDIS P.ClustHOSVD:Item Recommendation by Com- bining Semantically Enhanced Tag Clustering With Tensor HOSVD[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2016,46(9):1240-1251. [9]MISTRY O,SEN S.Tag recommendation for social bookmar- king:Probabilistic approaches [J].Multiagent and Grid Systems,2012,8(2):143-163. [10]于洪,李俊华.一种解决新项目冷启动问题的推荐算法[J].软件学报,2015,26(6):1395-1408. [11]ZHANG Z K,LIU C,ZHANG Y C,et al.Solving the cold-start problem in recommender systems with social tags [J].EPL (Europhysics Letters),2010,92(2):28002. [12]ZHANG Z K,ZHOU T,ZHANG Y C.Tag-Aware recommender systems:A state-of-the-art survey [J].Journal of Computer Science and Technology,2011,26(5):767-777. [13]ZHANG Z K,ZHOU T,ZHANG Y C.Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs[J].Physica A:Statistical Mechanics and its Applications,2010,389(1):179-186. [14]刘建勋,石敏,周栋,等.基于主题模型的Mashup标签推荐方法[J].计算机学报,2017,40(2):520-534. [15]李锡荣,许洁萍,薛盛博,等.基于软近邻投票的图像标签相关性计算[J].计算机学报,2014,37(6):1365-1371. [16]张斌,张引,高克宁,等.融合关系与内容分析的社会标签推荐[J].软件学报,2012,23(3):476-488. [17]YANG S,LU Z,GILES C L.Automatic tag recommendation algorithms for social recommender systems [J].ACM Transactions on the Web,2011,5(1):1-31. [18]孔欣欣,苏本昌,王宏志,等.基于标签权重评分的推荐模型及算法研究[J].计算机学报,2017,40(6):1440-1452. [19]JOMSRI P,SANGUANSINTUKUL S,CHOOCHAIWATTANA W.A framework for tag-based research paper recommender system:An IR approach[C]∥Proceedings of the 2010 IEEE 24th Int’l Conf.on Advanced Information Networking and Applications Workshops.2010:103-108. [20]郭彩云,王会进.改进的基于标签的协同过滤算法[J].计算机工程与应用,2016,52(8):56-61,147. [21]李慧,马小平,胡云,等.融合主题与语言模型的个性化标签推荐方法研究[J].计算机科学,2015,42(8):70-74. [22]AR Y,BOSTANCI E.A genetic algorithm solution tothe colla- borative filtering problem[J].Expert Systemswith Applications,2016,61:122-128. [23]李瑞敏,林鸿飞,闫俊.基于用户-标签-项目语义挖掘的个性化音乐推荐[J].计算机研究与发展,2014,51(10):2270-2276. [24]BREESE J S,HECKERMAN D,KADIE C.Empirical analysis of predictive algorithms for collaborative filtering[C]∥Proc.of the 14th Conference on Uncertainty in Artificial Intelligence.Madison,Wisconsin,USA,1998:43-52. [25]HERLOCKER J L,KONSTAN J A,BORCHERS A,et al.An algorithmic framework for performing collaborative filtering[C]∥22th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Berkeley,CA,USA,1999:230-237. [26]JIN R,CHAI J Y,SI L.An automatic weighting scheme for collaborative filtering[C]∥27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.Sheffield,UK,2004:337-344. [27]RESNICK P,IACOVOU N,SUCHAK M,et al.An open architecture for collaborative filtering of netnews[C]∥1994 ACM Conference on Computer Supported Cooperative Work.Chapel Hill,NC,USA,1994:175-186. [28]SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]∥10th International Conference on World Wide Web.Hong Kong,China,2001:285-295. [29]DESHPANDE M,KARYPIS G.Item-based top-n recommendation algorithms[J].ACM Transactions on Information System,2004,22(1):143-177. |
| [1] | CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan. Survey of Recommender Systems Based on Graph Learning [J]. Computer Science, 2022, 49(9): 1-13. |
| [2] | WANG Guan-yu, ZHONG Ting, FENG Yu, ZHOU Fan. Collaborative Filtering Recommendation Method Based on Vector Quantization Coding [J]. Computer Science, 2022, 49(9): 48-54. |
| [3] | SUN Xiao-han, ZHANG Li. Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace [J]. Computer Science, 2022, 49(7): 50-56. |
| [4] | CAI Xiao-juan, TAN Wen-an. Improved Collaborative Filtering Algorithm Combining Similarity and Trust [J]. Computer Science, 2022, 49(6A): 238-241. |
| [5] | HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279. |
| [6] | GUO Liang, YANG Xing-yao, YU Jiong, HAN Chen, HUANG Zhong-hao. Hybrid Recommender System Based on Attention Mechanisms and Gating Network [J]. Computer Science, 2022, 49(6): 158-164. |
| [7] | DONG Dan-dan, SONG Kang. Performance Analysis on Reconfigurable Intelligent Surface Aided Two-way Internet of Things Communication System [J]. Computer Science, 2022, 49(6): 19-24. |
| [8] | HU Fu-yuan, WAN Xin-jun, SHEN Ming-fei, XU Jiang-lang, YAO Rui, TAO Zhong-ben. Survey Progress on Image Instance Segmentation Methods of Deep Convolutional Neural Network [J]. Computer Science, 2022, 49(5): 10-24. |
| [9] | DONG Xiao-mei, WANG Rui, ZOU Xin-kai. Survey on Privacy Protection Solutions for Recommended Applications [J]. Computer Science, 2021, 48(9): 21-35. |
| [10] | ZHAN Wan-jiang, HONG Zhi-lin, FANG Lu-ping, WU Zhe-fu, LYU Yue-hua. Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning [J]. Computer Science, 2021, 48(7): 172-177. |
| [11] | SHAO Chao, SONG Shu-mi. Collaborative Filtering Recommendation Algorithm Based on User Preference Under Trust Relationship [J]. Computer Science, 2021, 48(6A): 240-245. |
| [12] | YU Sheng, LI Bin, SUN Xiao-bing, BO Li-li, ZHOU Cheng. Approach for Knowledge-driven Similar Bug Report Recommendation [J]. Computer Science, 2021, 48(5): 91-98. |
| [13] | WU Jian-xin, ZHANG Zhi-hong. Collaborative Filtering Recommendation Algorithm Based on User Rating and Similarity of Explicit and Implicit Interest [J]. Computer Science, 2021, 48(5): 147-154. |
| [14] | XIAO Shi-tao, SHAO Ying-xia, SONG Wei-ping, CUI Bin. Hybrid Score Function for Collaborative Filtering Recommendation [J]. Computer Science, 2021, 48(3): 113-118. |
| [15] | HAO Zhi-feng, LIAO Xiang-cai, WEN Wen, CAI Rui-chu. Collaborative Filtering Recommendation Algorithm Based on Multi-context Information [J]. Computer Science, 2021, 48(3): 168-173. |
|
||
Home