[1] |
Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry[J].Communications of ACM, 1992, 35(12): 61-70.
|
[2] |
Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering[J]. IEEE Internet Computing, 2003, 7(1): 76-80.
|
[3] |
Su X Y, Khoshgoftaar T M. A survey of collaborative filtering techniques[J]. Advances in Artificial Intelligence, 2009, 4: 1-19.
|
[4] |
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.
|
[5] |
Su X Y, Khoshgoftaar T M. Collaborative filtering for multi-class data using belief nets algorithms[C]// Proceedings of the International Conference on Tools with Artificial Intelligence. Arlington, USA: IEEE Computer Society, 2006: 497-504.
|
[6] |
Yu K, Schwaighofer A, Tresp V, et al. Probabilistic memory-based collaborative filtering[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(1): 56-69.
|
[7] |
Ben J, Dan F, Jon H. The Adaptive Web: Methods and Strategies of Web Personalization[M]. Berlin Heidelberg: Springer, 2004.
|
[8] |
Lü L, Medo M, et al. Recommender systems[J]. Physics Reports, 2012, 519(1): 1-49.
|
[9] |
Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
|
[10] |
Huang Z, Zeng D, Chen H. A comparative study of recommendation algorithms in e-commerce applications[J]. IEEE Intelligent Systems, 2007, 22(5): 68-78.
|
[11] |
García E, Romero C, Ventura S, et al. An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering[J].User Modeling and User-Adapted Interaction, 2009, 19(1-2): 99-132.
|
[12] |
Sarwar B, Karypis G, Konstan J, et al. Analysis of recommendation algorithms for E-commerce[C]// Proceedings of the ACM E-Commerce. NewYork, USA: ACM Press, 2000: 158-167.
|
[13] |
Leung C W K, Chan S C F, Chung F L. A collaborative filtering framework based on fuzzy association rules and multi-level similarity[J]. Knowledge and Information Systems, 2006, 10(3): 357-381.
|
[14] |
Leung C W K, Chan S C F, Chung F L. Applying cross-level association rule mining to cold-start recommendations[C]// Proceeding of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops. Silicon Valley, USA: IEEE Press, 2007:133-136.
|
[15] |
Leung C W K, Chan S C F, Chung F L. An empirical study of a cross-level association rule mining approach to cold-start recommendations[J]. Knowledge-Based Systems, 2008, 21(7): 515-529.
|
[16] |
Lin W, Alvarez S A, Ruiz C. Efficient adaptive-support association rule mining for recommender systems[J]. Data Mining and Knowledge Discovery, 2014, 6(1): 83-105.
|
[17] |
Shaw G, Xu Y, Geva S. Using association rules to solve the cold-start problem in recommender systems[C]// Proceeding of the 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Berlin: Springer, 2014: 340-347.
|
[18] |
Sobhanam H, Mariappan A K. Addressing cold start problem in recommender systems using association rules and clustering technique[C]// Proceeding of the International Conference on Computer Communication and Informatics. Coimbatore: IEEE press, 2013:1-5.
|
[19] |
Khanzadeh Z, Mahdavi M. Utilizing association rules for improving the performance of collaborative filtering[J]. International Journal of E-Entrepreneurship and Innovation, 2012, 3(2): 14-28.
|
[20] |
Tyagi S, Bharadwaj K K. Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining[J]. Swarm and Evolutionary Computation, 2013, 13: 1-12.
|
[21] |
Tyagi S, Bharadwaj K K. Enhanced new user recommendations based on quantitative association rule mining[J]. Procedia Computer Science, 2012, 10: 102-109.
|
[22] |
Ye H W. A personalized collaborative filtering recommendation using association rules mining and self-organizing map[J]. Journal of Software, 2011, 6(4): 732-739.
|
[23] |
Yang H. Improved collaborative filtering recommendation algorithm based on weighted association rules[J]. Applied Mechanics and Materials, 2013, (411-414): 94-97.
|
[24] |
郭晓波, 赵书良, 王长宾, 等. 一种新的面向普通用户的多值属性关联规则可视化挖掘方法[J]. 电子学报, 2015, 43(2): 344-352.Guo X B, Zhao S L, Wang C B, et al. A new visualizing mining method of Multi-valued attribute association rules for ordinary users[J]. Acta Electronica Sinica, 2015, 43(23): 344-352.
|
[25] |
Sarwar B M, Karypis G, Konstan J A, et al. Item-based collaborative filtering recommendation algorithms[C]// Proceedings of the 10th International Conference on World Wide Web. New York, USA: ACM Press, 2001: 285-295.
|
[26] |
Breese J, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering[C]// Proceeding of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 1998: 43-52.
|
[27] |
MovieLens Dataset[EB/OL]. http://www.grouplens.org/datasets/movielens/.
|
[28] |
Koren Y. Factorization meets the neighborhood: A multifaceted collaborative filtering model[C]// Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, USA: ACM Press, 2008: 426-434.
|
[29] |
Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering[C]// Proceedings of the 24th International Conference on Machine learning. Corvallis, USA: ACM Press, 2007: 791-798.)
|
[1] |
Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry[J].Communications of ACM, 1992, 35(12): 61-70.
|
[2] |
Linden G, Smith B, York J. Amazon.com recommendations: Item-to-item collaborative filtering[J]. IEEE Internet Computing, 2003, 7(1): 76-80.
|
[3] |
Su X Y, Khoshgoftaar T M. A survey of collaborative filtering techniques[J]. Advances in Artificial Intelligence, 2009, 4: 1-19.
|
[4] |
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.
|
[5] |
Su X Y, Khoshgoftaar T M. Collaborative filtering for multi-class data using belief nets algorithms[C]// Proceedings of the International Conference on Tools with Artificial Intelligence. Arlington, USA: IEEE Computer Society, 2006: 497-504.
|
[6] |
Yu K, Schwaighofer A, Tresp V, et al. Probabilistic memory-based collaborative filtering[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(1): 56-69.
|
[7] |
Ben J, Dan F, Jon H. The Adaptive Web: Methods and Strategies of Web Personalization[M]. Berlin Heidelberg: Springer, 2004.
|
[8] |
Lü L, Medo M, et al. Recommender systems[J]. Physics Reports, 2012, 519(1): 1-49.
|
[9] |
Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
|
[10] |
Huang Z, Zeng D, Chen H. A comparative study of recommendation algorithms in e-commerce applications[J]. IEEE Intelligent Systems, 2007, 22(5): 68-78.
|
[11] |
García E, Romero C, Ventura S, et al. An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering[J].User Modeling and User-Adapted Interaction, 2009, 19(1-2): 99-132.
|
[12] |
Sarwar B, Karypis G, Konstan J, et al. Analysis of recommendation algorithms for E-commerce[C]// Proceedings of the ACM E-Commerce. NewYork, USA: ACM Press, 2000: 158-167.
|
[13] |
Leung C W K, Chan S C F, Chung F L. A collaborative filtering framework based on fuzzy association rules and multi-level similarity[J]. Knowledge and Information Systems, 2006, 10(3): 357-381.
|
[14] |
Leung C W K, Chan S C F, Chung F L. Applying cross-level association rule mining to cold-start recommendations[C]// Proceeding of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops. Silicon Valley, USA: IEEE Press, 2007:133-136.
|
[15] |
Leung C W K, Chan S C F, Chung F L. An empirical study of a cross-level association rule mining approach to cold-start recommendations[J]. Knowledge-Based Systems, 2008, 21(7): 515-529.
|
[16] |
Lin W, Alvarez S A, Ruiz C. Efficient adaptive-support association rule mining for recommender systems[J]. Data Mining and Knowledge Discovery, 2014, 6(1): 83-105.
|
[17] |
Shaw G, Xu Y, Geva S. Using association rules to solve the cold-start problem in recommender systems[C]// Proceeding of the 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Berlin: Springer, 2014: 340-347.
|
[18] |
Sobhanam H, Mariappan A K. Addressing cold start problem in recommender systems using association rules and clustering technique[C]// Proceeding of the International Conference on Computer Communication and Informatics. Coimbatore: IEEE press, 2013:1-5.
|
[19] |
Khanzadeh Z, Mahdavi M. Utilizing association rules for improving the performance of collaborative filtering[J]. International Journal of E-Entrepreneurship and Innovation, 2012, 3(2): 14-28.
|
[20] |
Tyagi S, Bharadwaj K K. Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining[J]. Swarm and Evolutionary Computation, 2013, 13: 1-12.
|
[21] |
Tyagi S, Bharadwaj K K. Enhanced new user recommendations based on quantitative association rule mining[J]. Procedia Computer Science, 2012, 10: 102-109.
|
[22] |
Ye H W. A personalized collaborative filtering recommendation using association rules mining and self-organizing map[J]. Journal of Software, 2011, 6(4): 732-739.
|
[23] |
Yang H. Improved collaborative filtering recommendation algorithm based on weighted association rules[J]. Applied Mechanics and Materials, 2013, (411-414): 94-97.
|
[24] |
郭晓波, 赵书良, 王长宾, 等. 一种新的面向普通用户的多值属性关联规则可视化挖掘方法[J]. 电子学报, 2015, 43(2): 344-352.Guo X B, Zhao S L, Wang C B, et al. A new visualizing mining method of Multi-valued attribute association rules for ordinary users[J]. Acta Electronica Sinica, 2015, 43(23): 344-352.
|
[25] |
Sarwar B M, Karypis G, Konstan J A, et al. Item-based collaborative filtering recommendation algorithms[C]// Proceedings of the 10th International Conference on World Wide Web. New York, USA: ACM Press, 2001: 285-295.
|
[26] |
Breese J, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering[C]// Proceeding of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 1998: 43-52.
|
[27] |
MovieLens Dataset[EB/OL]. http://www.grouplens.org/datasets/movielens/.
|
[28] |
Koren Y. Factorization meets the neighborhood: A multifaceted collaborative filtering model[C]// Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, USA: ACM Press, 2008: 426-434.
|
[29] |
Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering[C]// Proceedings of the 24th International Conference on Machine learning. Corvallis, USA: ACM Press, 2007: 791-798.)
|