Li Y E, Zhai C X, Chen Y. Exploiting Rich User Information for One-Class Collaborative Filtering[EB/OL].[2014-02-20].http://link.springer.com/article/10.1007/s10115-012-0583-9/fulltext.html
[2]
Goldberg D, Nichols D, Oki B M, et al. Using Collaborative Fil-tering to Weave an Information Tapestry. Communications of the ACM, 1992, 35(12): 61-70
[3]
Lin Y J, Hu X G, Li H Z. Collaborative Filtering Recommendation Algorithm Based on User Group Influence. Journal of the China Society for Scientific and Technical Information, 2013, 32(3): 299-305 (in Chinese)(林耀进,胡学钢,李慧宗.基于用户群体影响的协同过滤推荐算法.情报学报, 2013, 32(3): 299-305)
[4]
Shi Y, Larson M, Hanjalic A. Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Cha-llenges. ACM Computing Surveys, 2014. DOI: 10.1145/2556270
[5]
Sun X H. Research of Sparsity and Cold Start Problem in Collaborative Filtering. Ph. D Dissertation. Hangzhou, China: Zhejiang Un-iversity, 2005 (in Chinese)(孙小华.协同过滤系统的稀疏性与冷启动问题研究.博士学位论文.杭州:浙江大学, 2005)
[6]
Deng A L, Zhu Y Y, Shi B L. A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction. Journal of Software, 2003, 14(9): 1621-1628 (in Chinese)(邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法.软件学报, 2003, 14(9): 1621-1628)
[7]
Li C, Liang C Y, Ma L. A Collaborative Filtering Recommendation Algorithm Based on Domain Nearest Neighbor. Journal of Computer Research and Development, 2008, 45(9): 1532-1538 (in Chinese)(李 聪,梁昌勇,马 丽.基于领域最近邻的协同过滤推荐算法.计算机研究与发展, 2008, 45(9): 1532-1538)
[8]
Leng Y J, Liang C Y, Ding Y, et al. Method of Neighborhood Formation in Collaborative Filtering. Pattern Recognition and Artificial Intelligence, 2013, 26(10): 968-974 (in Chinese)(冷亚军,梁昌勇,丁 勇,等.协同过滤中一种有效的最近邻选择方法.模式识别与人工智能, 2013, 26(10): 968-974)
[9]
Ahn H J. A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold-Starting Problem. Information Sciences, 2008, 178(1): 37-51
[10]
Zhang F Z, Chang J F, Wang D. Multi-criteria Recommendation Algorithm Based on Widrow-Hoff Neural Network. Pattern Reco-gnition and Artificial Intelligence, 2011, 24(2): 233-242 (in Chinese)(张付志,常俊风,王 栋.基于Widrow-Hoff 神经网络的多指标推荐算法.模式识别与人工智能, 2011, 24(2): 233-242)
[11]
Bobadilla J, Hernando A, Ortega F, et al. Collaborative Filtering Based on Significances. Information Sciences, 2012, 185: 1-17
[12]
Jeong B, Lee J, Cho H. User Credit-Based Collaborative Filtering. Expert Systems with Applications, 2009, 36(3): 7309-7312
[13]
Anand D, Bharadwaj K K. Utilizing Various Sparsity Measures for Enhancing Accuracy of Collaborative Recommender Systems Based on Local and Global Similarities. Expert Systems with Applications, 2011, 38(5): 5101-5109
[14]
Bobadilla J, Hernando A, Ortega F, et al. A Framework for Co-llaborative Filtering Recommender Systems. Expert Systems with Applications, 2011, 38(12): 14609-14623
[15]
Bobadilla J, Ortega F, Hernando A, et al. Recommender Systems Survey. Knowledge-Based Systems, 2013, 46: 109-132
[16]
Breese J, Heckerman D, Kadie C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering[EB/OL].[2014-02-20].http://www.cs.rutgers.edu/~mlittman/course/mlo3/iCML03/papers/ruoheng.pdf
[17]
Resnick P, Iacovou N, Suchak M, et al. GroupLens: An Open Architecture for Collaborative Filtering of Netnews // Proc of the ACM Conference on Computer Supported Cooperative Work. Cha-pel Hill, USA, 1994: 175-186
[18]
Liu H F, Hu Z, Mian A, et al. A New User Similarity Model to Improve the Accuracy of Collaborative Filtering. Knowledge-Based Systems, 2014, 56: 156-166
