电子商务个性化推荐系统中协同过滤算法的研究与应用
|
摘要
随着网络技术的发展和不断完善,电子商务已经覆盖了整个营销网络,成为一种新的商务通信通道,为了使用户得到更好的购买体验,个性化的推荐系统渐渐兴起。个性化推荐系统对网站的历史记录进行分析,挖掘用户兴趣,预测并推荐用户可能感兴趣的商品。协同过滤技术是个性化推荐系统中较常用的推荐技术,但是随着个性化推荐系统的发展及应用,该技术存在的稀疏性、冷启动、可扩展性等问题日益成为推荐的瓶颈。
经过深入的研究和分析,本文对传统的协同过滤算法进行了改进。改进的方法是首先在概念分层技术的基础上,采用项目评分度等概念,对用户-项目评分矩阵进行缩减,构建用户-项目评分候选集,提高推荐算法的可扩展性；其次引入“项目客观特征偏向”和“兴趣偏向度”概念,并对稀疏的用户-项目评分候选集进行更加合理的填充,以缓解数据稀疏性和冷启动问题带来的影响；最后在预测项目评分时,使用用户对一类项目的评分平均值来度量用户的评分习惯,提出基于项目类评分尺度的推荐公式,以提高预测准确性。
经过实验及各评价规则上的测试分析,改进的协同过滤算法较传统协同过滤算法有更佳的推荐效果。最后本文选择了电子商务个性化推荐系统的典型案例——图书推荐系统进行开发和实现,将改进的协同过滤算法应用到图书推荐服务中,进一步核准了此算法的实用性。
Along with the development and perfection of network technology, E-Commerce has covered the whole marketing network and become a new channel in business communication. For better user experience, Personalized Recommendation Systems gradually rise. It is used to analysis user's historical records, mine user's interest; provide prediction and recommendation to the target user. Collaborative Filtering algorithm is one of the common recommendation technologies, but with the development and application of the Personalized Recommendation System, these problems, such as sparsity, cold start, expansibility etc, have become bottlenecks in recommendation.
Through the in-depth research and analysis, this paper presents the improved algorithm based on the traditional Cooperative Filtering. The method is that firstly on basis of Concept Hierarchy, adopt the concept of cluster rated degree, reduce user-item score matrix, get the smaller candidate matrix to improve recommendation scalability; Secondly, these concepts "Item Objective Character" and "Interest Deviation Degree" are introduced to fill the sparse candidate matrix more reasonably, alleviate the impact of data sparsity and cold start; Finally, in prediction, the average of rating to itemset is used to measure user's rating habit and a new recommend formula based on itemset's rating is put forward to improve the prediction accuracy.
Through experiments and the test on evaluation rules, the improved algorithm is proved to be better than the traditional collaborative filtering algorithm in recommendation. Finally, realize a book recommendation system as the typical case of the Recommendation Systems for E-Commerce, the improved cooperative filtering algorithm is used in the service of book recommendation, and the practicality of improved algorithm is approved.
经过深入的研究和分析,本文对传统的协同过滤算法进行了改进。改进的方法是首先在概念分层技术的基础上,采用项目评分度等概念,对用户-项目评分矩阵进行缩减,构建用户-项目评分候选集,提高推荐算法的可扩展性；其次引入“项目客观特征偏向”和“兴趣偏向度”概念,并对稀疏的用户-项目评分候选集进行更加合理的填充,以缓解数据稀疏性和冷启动问题带来的影响；最后在预测项目评分时,使用用户对一类项目的评分平均值来度量用户的评分习惯,提出基于项目类评分尺度的推荐公式,以提高预测准确性。
经过实验及各评价规则上的测试分析,改进的协同过滤算法较传统协同过滤算法有更佳的推荐效果。最后本文选择了电子商务个性化推荐系统的典型案例——图书推荐系统进行开发和实现,将改进的协同过滤算法应用到图书推荐服务中,进一步核准了此算法的实用性。
Along with the development and perfection of network technology, E-Commerce has covered the whole marketing network and become a new channel in business communication. For better user experience, Personalized Recommendation Systems gradually rise. It is used to analysis user's historical records, mine user's interest; provide prediction and recommendation to the target user. Collaborative Filtering algorithm is one of the common recommendation technologies, but with the development and application of the Personalized Recommendation System, these problems, such as sparsity, cold start, expansibility etc, have become bottlenecks in recommendation.
Through the in-depth research and analysis, this paper presents the improved algorithm based on the traditional Cooperative Filtering. The method is that firstly on basis of Concept Hierarchy, adopt the concept of cluster rated degree, reduce user-item score matrix, get the smaller candidate matrix to improve recommendation scalability; Secondly, these concepts "Item Objective Character" and "Interest Deviation Degree" are introduced to fill the sparse candidate matrix more reasonably, alleviate the impact of data sparsity and cold start; Finally, in prediction, the average of rating to itemset is used to measure user's rating habit and a new recommend formula based on itemset's rating is put forward to improve the prediction accuracy.
Through experiments and the test on evaluation rules, the improved algorithm is proved to be better than the traditional collaborative filtering algorithm in recommendation. Finally, realize a book recommendation system as the typical case of the Recommendation Systems for E-Commerce, the improved cooperative filtering algorithm is used in the service of book recommendation, and the practicality of improved algorithm is approved.
引文
[1]Meadow C T Bert R, Boyce B R,Kraft D H. Text Information Retrieval Systems Third Edition [M].:Emerald Group Publishing Limited,2007.
[2]Belkin NJ Croft WB. Information filtering and information retrieval:two sides of the same coin [J]. Communications of the ACM,1992,
[3]Manning C D Raghavan P, Schutze H. Introduction to Information Retrieva [MJ.England: Cambridge University Press,2008.
[4]Schafer J.B., Konstan,J.A.,Ried,J. E-commerce recommendation applications [J]. Data Mining and Konwledge Discovery,2001,5(1-2) 115-153.
[5]Sarwar B M. Sparsity, scalability, and distribution in recommender systems [D]. University of Minnesota,2001.
[6]Resnick P Varian J. Recommendation systems [J]. Communications of the ACM,1997,40 (3):
[7]He Keqin He Liang, Xia Weiwei. A New Effective Collaborative Filtering Algorithm Based on User's Interest Partition[C].Computer Science and Computational Technology,2008. ISCSCT'08. International Symposium on.20-22 Dec.2008,727-731
[8]黄合鑫.电子商务协同过滤算法的研究与应用[D].北京交通大学,2011.
[9]Mikolajczyk K Schmid C. A performance Evaluation of Local Descriptors [J]. On Pattern Analysis and Machine Intelligence,2005, (27):1615-1630.
[10]Goldberg D Nichols D, Oki B M. Using Collaborative Filtering to Weave an Information Tapestry [J]. Communications of the ACM.1992,35 (12):
[11]Konstan Joseph A Miller Bradley N, Maltz David.et al. GroupLens:applying collaborative filtering to Usenet news [J]. Communications of the ACM,1997,40 (3): 77-87.
[12]Resnick Paul lacovou Neophytos. Suchak Mitesh,et al.GroupLens:An open architecture for collaborative filtering of netnews[C].Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work.Chapel Hill:ACM,1994.175
[13]Bresse J.S Heckerman D, Kadie C.Empirical analysis of predictive algorithms for collaborative filtering[C].Proc of UAI.1998.
[14]Billsus D Pazzani M.J. Learning collaborative information filters[C]. International Conference on Machine Learning(ICML).1998,
[15]O'Mahony Michael P; Hurley, Neil J; Silvestre, Guenole C.M.Utility-based neighbourhood formation for efficient and robust collaborative filtering[C].Proceedings of the 5th ACM Conference on Electronic Commerce, EC'04.New York:Association for Computing Machinery.2004.260-261
[16]Linden Greg Smith Brent, York Jeremy Amazon.com recommendations:Item-to-item collaborative filtering [J]. IEEE Internet Computing,2003,7(1):76-80.
[17]孙小华.协同过滤系统的稀疏性与冷启动问题研究[D].浙江大学.2005.
[18]郭艳红.推荐系统的协同过滤算法与应用研究[D].大连理工大学，2008.
[19]Balabanovic M.An Adaptive Web Page Recommendation Service[C].Proceedings of the First International Conference on Autonomous Agents.Marina del Rey:ACM Press,378-385
[20]余力刘鲁,罗掌华.我国电子商务推荐策略的比较分析[J].系统工程理论与实践,2004,(08)：96-101.
[21]曾小波.基于协同过滤的推荐系统的研究[D].电子科技大学,2010.
[22]曾春邢春晓,周立柱.个性化服务技术综述[J].软件学报,2002,13(10)：1952-1961.
[23]Claypool M Le P, Waseda M.Implicit interest indicators[C].Proceedings of the 6th International Conference on Intelligent User Interfaces.New York:ACM Press,2001,33-40
[24]Papagelis M Plexousakis D, Rousidis I Qualitative analysis of user-based and item-based prediction algorithms for recommendation systems[C].Proceedings of the 3rd Hellenic Data Management Symposium.2004,81-90
[25]马丽.电子商务个性化推荐技术分析及比较[J].计算机系统应用,2008,17(12)：58-61.
[26]Jiawei Han Micheline Kamber.数据挖掘概念与技术机械工业出版社，2007.
[27]黄晓斌.网络信息过滤原理与应用北京图书馆出版社，2005.
[28]Balabanovic M Shoham Y. Fab:Content-based collaborative recommendation [J]. Communications of the ACM,1997,40 (3):66-72.
[29]Sarwar B Karypis G, Konstan J.etal. Item-Based Collaborative Filtering Recommendation Algorithms [J]. Proceedings of the 10th International Conference on World Wide Web (WWW'01).2001.285-295.
[30]Khoshgoftaar Xiaoyuan Su, Taghi M. A survey of collaborative filtering techniques [J]. Advances in Artificial Intelligence,2009,2009
[31]Burke Robin. Hybrid Recommender Systems:Survey and Experiments [J]. USER MODELING AND USER-ADAPTED INTERACTION,2002,12 (4):331-370.
[32]Herlocker J L Konstan J A, Terveen L Get al. Evaluating collaborative filtering recommender systems [J]. ACM Transactions on Information Systems,2004.22 (1):5-53.
[33]Sarwar Badrul Karypis George, Konstan Joseph Application of Dimensionality Reduction in Recommender System-A Case Study[C].Proceedings of the ACM WebKDD 2000 Workhop on Web Mining For E-Commerce.Boston:ACM Press,2000,
[34]Sarwar B Karypis G, Konstan J.etal.Analysis of recommendation algorithms for e-commerce[C].The 2nd ACM conference on Electronic commerce.Minneapolis:ACM.2000.158-167
[35]Jon Herlocker Joseph A. Konstan, John Riedl. An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms [J]. INFORMATION RETRIEVAL,2002,5 (4):287-310.
[36]Adomavicius G Tuzhilin A. Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions [J]. Knowledge and Data Engineering,2005, 17 (6):734-749.
[37]夏建勋.基于用户的协同过滤推荐技术[J].商场现代化.2009,9
[38]Sarwar B Karypis G, Konstan J.etal.Item-based collaborative filtering recommendation algorithms[C].The 10th International World Wide Web Conference.Hong Kong:
[39]Zan Huang Hsinchun Chen, Daniel Zeng. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering [J]. ACM Transactions on Information Systems,2004.22 (1):116-142.
[40]吴丽花刘鲁.个性化推荐系统用户建模技术综述[J].情报学报.2006，25(1)：55-62.
[41]周军锋汤显,郭景峰.一种优化的协同过滤推荐算法[J].计算机研究与发展,2004，41(10)：1842-1847.
[42]Chun Zeng Chun-Xiao Xing, Li-Zhu Zhou. Similarity Measure and Instance Selection for Collaborative Filtering [J]. International journal of electronic commerce,2004,8 (4): 652-658.
[43]Jasson D M Nathan Srebro.Fast maximum margin matrix factorization for collaborative prediction[C].ICML'05 Proceedings of the 22nd international conference on Machine learning.New York:ACM.2005,713-719
[44]Deerwester S Dumais S T, Furnas G W. Indexing by latent semantic analysis [J]. American Society for Information Science,1990,41 (6):391-407.
[45]陈华李仁发,刘钰峰.个性化搜索引擎推荐算法研究[J].计算机应用研究,2010，27(1)：48-50.
[46]顾中华.结合奇异值分解和时间权重的协同过滤算法[J].计算机应用与软件,2010，27(6)：256-259.
[47]Vozalis M G Margaritis K G Using SVD and demographic data for the enhancement of generalized Collaborative Filtering [J]. Information Sciences,2007,177 (15):3017-3037.
[48]曾小波 魏祖宽,金在弘.协同过滤系统的矩阵稀疏性问题的研究[J].计算机应用,2010,30(4)：1079-1082.
[49]徐仔波陈文超,高静怀.基于独立成分分析的改进SVD算法研究[C].中国地球物理学会第二十四届年会.北京：2008,168-168
[50]Manos Papagelis Dimitris Plexousakis, Themistoklis Kutsuras.Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences[C].TRUST MANAGEMENT.Berlin:Springer-Verlag.2005,224-239
[51]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[J].软件学报，2003，14(9)：1621-1628.
[52]YiBo Ren SongJie Gong.A Collaborative Filtering Recommendation Algorithm Based on SVD Smoothing[C].3rd Intelligent Information Technology Application.2009.Nanchang 2009,530-532
[53]张锋常会友.使用BP神经网络缓解协同过滤推荐算法的稀疏性问题[J].计算机研究与发展，2006,43(4)：667-672.
[54]张其文李明.一种缺失数据的填补方法[J].兰州理工大学学报，2006,32(02)：102-104.
[55]Su Ping Ye Hong Wu.An item based collaborative filtering recommendation algorithm using rough set prediction[C].1st IITA International Joint Conference on Artificial Intelligence, JCAI 2009.Hainan Island, China:International Joint Conferences on Artificial Intelligence, United States.2009.308-311
[56]Sarwar B Konstan J, Borchers Al.etal.Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system[C].CSCWComputer Supported Cooperative Work.New York:ACM,345-354
[57]A1 Mamunur Rashid Istvan Albert, Dan Cosley.Getting to know you:learning new user preferences in recommender systems[C].IUI'02 Proceedings of the 7th international conference on Intelligent user interfaces.New York:ACM,2002,127-134
[58]吴婷.基于用户特征和用户兴趣变化的协同过滤推荐[J].电脑知识与技术,2008.4 (7)：1730-1732.
[59]徐莉.结合人口分类特征知识的协同过滤算法[J].安徽科技学院学报,2007，21(2)：40-45.
[60]荆玲.基于遗传算法的实时个性化推荐研究[D].重庆大学,2003.
[61]张海燕丁奉,姜丽红.基于模糊聚类的协同过滤推荐方法[J].计算机仿真,2005,22(8)：144-147.
[62]Hofmann Thomas. Latent semantic models for collaborative filtering [J]. ACM Transactions on Information Systems,2004,22 (1):89-115.
[63]孟宪福 陈莉.基于贝叶斯理论的协同过滤推荐算法[J].计算机应用，2009,29(10)：2733-2735.
[64]李大学谢名亮,赵学斌.基于朴素贝叶斯方法的协同过滤推荐算法[J].计算机应用,2010，30(6)：1523-1526.
[65]傅鹤岗王竹伟.对基于项目的协同过滤推荐系统的改进[J].重庆理工大学学报，2010，24(9)：70-74.
[66]陈健印鉴.基于影响集的协作过滤推荐算法[J].软件学报,2007，18(7)：1685-1694.
[67]Lemire D Maclachlan A.Slope one predictors for online rating-based collaborative filtering[C].The 5th SIAM International Conference on Data Mining.2005,471-476
[2]Belkin NJ Croft WB. Information filtering and information retrieval:two sides of the same coin [J]. Communications of the ACM,1992,
[3]Manning C D Raghavan P, Schutze H. Introduction to Information Retrieva [MJ.England: Cambridge University Press,2008.
[4]Schafer J.B., Konstan,J.A.,Ried,J. E-commerce recommendation applications [J]. Data Mining and Konwledge Discovery,2001,5(1-2) 115-153.
[5]Sarwar B M. Sparsity, scalability, and distribution in recommender systems [D]. University of Minnesota,2001.
[6]Resnick P Varian J. Recommendation systems [J]. Communications of the ACM,1997,40 (3):
[7]He Keqin He Liang, Xia Weiwei. A New Effective Collaborative Filtering Algorithm Based on User's Interest Partition[C].Computer Science and Computational Technology,2008. ISCSCT'08. International Symposium on.20-22 Dec.2008,727-731
[8]黄合鑫.电子商务协同过滤算法的研究与应用[D].北京交通大学,2011.
[9]Mikolajczyk K Schmid C. A performance Evaluation of Local Descriptors [J]. On Pattern Analysis and Machine Intelligence,2005, (27):1615-1630.
[10]Goldberg D Nichols D, Oki B M. Using Collaborative Filtering to Weave an Information Tapestry [J]. Communications of the ACM.1992,35 (12):
[11]Konstan Joseph A Miller Bradley N, Maltz David.et al. GroupLens:applying collaborative filtering to Usenet news [J]. Communications of the ACM,1997,40 (3): 77-87.
[12]Resnick Paul lacovou Neophytos. Suchak Mitesh,et al.GroupLens:An open architecture for collaborative filtering of netnews[C].Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work.Chapel Hill:ACM,1994.175
[13]Bresse J.S Heckerman D, Kadie C.Empirical analysis of predictive algorithms for collaborative filtering[C].Proc of UAI.1998.
[14]Billsus D Pazzani M.J. Learning collaborative information filters[C]. International Conference on Machine Learning(ICML).1998,
[15]O'Mahony Michael P; Hurley, Neil J; Silvestre, Guenole C.M.Utility-based neighbourhood formation for efficient and robust collaborative filtering[C].Proceedings of the 5th ACM Conference on Electronic Commerce, EC'04.New York:Association for Computing Machinery.2004.260-261
[16]Linden Greg Smith Brent, York Jeremy Amazon.com recommendations:Item-to-item collaborative filtering [J]. IEEE Internet Computing,2003,7(1):76-80.
[17]孙小华.协同过滤系统的稀疏性与冷启动问题研究[D].浙江大学.2005.
[18]郭艳红.推荐系统的协同过滤算法与应用研究[D].大连理工大学，2008.
[19]Balabanovic M.An Adaptive Web Page Recommendation Service[C].Proceedings of the First International Conference on Autonomous Agents.Marina del Rey:ACM Press,378-385
[20]余力刘鲁,罗掌华.我国电子商务推荐策略的比较分析[J].系统工程理论与实践,2004,(08)：96-101.
[21]曾小波.基于协同过滤的推荐系统的研究[D].电子科技大学,2010.
[22]曾春邢春晓,周立柱.个性化服务技术综述[J].软件学报,2002,13(10)：1952-1961.
[23]Claypool M Le P, Waseda M.Implicit interest indicators[C].Proceedings of the 6th International Conference on Intelligent User Interfaces.New York:ACM Press,2001,33-40
[24]Papagelis M Plexousakis D, Rousidis I Qualitative analysis of user-based and item-based prediction algorithms for recommendation systems[C].Proceedings of the 3rd Hellenic Data Management Symposium.2004,81-90
[25]马丽.电子商务个性化推荐技术分析及比较[J].计算机系统应用,2008,17(12)：58-61.
[26]Jiawei Han Micheline Kamber.数据挖掘概念与技术机械工业出版社，2007.
[27]黄晓斌.网络信息过滤原理与应用北京图书馆出版社，2005.
[28]Balabanovic M Shoham Y. Fab:Content-based collaborative recommendation [J]. Communications of the ACM,1997,40 (3):66-72.
[29]Sarwar B Karypis G, Konstan J.etal. Item-Based Collaborative Filtering Recommendation Algorithms [J]. Proceedings of the 10th International Conference on World Wide Web (WWW'01).2001.285-295.
[30]Khoshgoftaar Xiaoyuan Su, Taghi M. A survey of collaborative filtering techniques [J]. Advances in Artificial Intelligence,2009,2009
[31]Burke Robin. Hybrid Recommender Systems:Survey and Experiments [J]. USER MODELING AND USER-ADAPTED INTERACTION,2002,12 (4):331-370.
[32]Herlocker J L Konstan J A, Terveen L Get al. Evaluating collaborative filtering recommender systems [J]. ACM Transactions on Information Systems,2004.22 (1):5-53.
[33]Sarwar Badrul Karypis George, Konstan Joseph Application of Dimensionality Reduction in Recommender System-A Case Study[C].Proceedings of the ACM WebKDD 2000 Workhop on Web Mining For E-Commerce.Boston:ACM Press,2000,
[34]Sarwar B Karypis G, Konstan J.etal.Analysis of recommendation algorithms for e-commerce[C].The 2nd ACM conference on Electronic commerce.Minneapolis:ACM.2000.158-167
[35]Jon Herlocker Joseph A. Konstan, John Riedl. An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms [J]. INFORMATION RETRIEVAL,2002,5 (4):287-310.
[36]Adomavicius G Tuzhilin A. Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions [J]. Knowledge and Data Engineering,2005, 17 (6):734-749.
[37]夏建勋.基于用户的协同过滤推荐技术[J].商场现代化.2009,9
[38]Sarwar B Karypis G, Konstan J.etal.Item-based collaborative filtering recommendation algorithms[C].The 10th International World Wide Web Conference.Hong Kong:
[39]Zan Huang Hsinchun Chen, Daniel Zeng. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering [J]. ACM Transactions on Information Systems,2004.22 (1):116-142.
[40]吴丽花刘鲁.个性化推荐系统用户建模技术综述[J].情报学报.2006，25(1)：55-62.
[41]周军锋汤显,郭景峰.一种优化的协同过滤推荐算法[J].计算机研究与发展,2004，41(10)：1842-1847.
[42]Chun Zeng Chun-Xiao Xing, Li-Zhu Zhou. Similarity Measure and Instance Selection for Collaborative Filtering [J]. International journal of electronic commerce,2004,8 (4): 652-658.
[43]Jasson D M Nathan Srebro.Fast maximum margin matrix factorization for collaborative prediction[C].ICML'05 Proceedings of the 22nd international conference on Machine learning.New York:ACM.2005,713-719
[44]Deerwester S Dumais S T, Furnas G W. Indexing by latent semantic analysis [J]. American Society for Information Science,1990,41 (6):391-407.
[45]陈华李仁发,刘钰峰.个性化搜索引擎推荐算法研究[J].计算机应用研究,2010，27(1)：48-50.
[46]顾中华.结合奇异值分解和时间权重的协同过滤算法[J].计算机应用与软件,2010，27(6)：256-259.
[47]Vozalis M G Margaritis K G Using SVD and demographic data for the enhancement of generalized Collaborative Filtering [J]. Information Sciences,2007,177 (15):3017-3037.
[48]曾小波 魏祖宽,金在弘.协同过滤系统的矩阵稀疏性问题的研究[J].计算机应用,2010,30(4)：1079-1082.
[49]徐仔波陈文超,高静怀.基于独立成分分析的改进SVD算法研究[C].中国地球物理学会第二十四届年会.北京：2008,168-168
[50]Manos Papagelis Dimitris Plexousakis, Themistoklis Kutsuras.Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences[C].TRUST MANAGEMENT.Berlin:Springer-Verlag.2005,224-239
[51]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[J].软件学报，2003，14(9)：1621-1628.
[52]YiBo Ren SongJie Gong.A Collaborative Filtering Recommendation Algorithm Based on SVD Smoothing[C].3rd Intelligent Information Technology Application.2009.Nanchang 2009,530-532
[53]张锋常会友.使用BP神经网络缓解协同过滤推荐算法的稀疏性问题[J].计算机研究与发展，2006,43(4)：667-672.
[54]张其文李明.一种缺失数据的填补方法[J].兰州理工大学学报，2006,32(02)：102-104.
[55]Su Ping Ye Hong Wu.An item based collaborative filtering recommendation algorithm using rough set prediction[C].1st IITA International Joint Conference on Artificial Intelligence, JCAI 2009.Hainan Island, China:International Joint Conferences on Artificial Intelligence, United States.2009.308-311
[56]Sarwar B Konstan J, Borchers Al.etal.Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system[C].CSCWComputer Supported Cooperative Work.New York:ACM,345-354
[57]A1 Mamunur Rashid Istvan Albert, Dan Cosley.Getting to know you:learning new user preferences in recommender systems[C].IUI'02 Proceedings of the 7th international conference on Intelligent user interfaces.New York:ACM,2002,127-134
[58]吴婷.基于用户特征和用户兴趣变化的协同过滤推荐[J].电脑知识与技术,2008.4 (7)：1730-1732.
[59]徐莉.结合人口分类特征知识的协同过滤算法[J].安徽科技学院学报,2007，21(2)：40-45.
[60]荆玲.基于遗传算法的实时个性化推荐研究[D].重庆大学,2003.
[61]张海燕丁奉,姜丽红.基于模糊聚类的协同过滤推荐方法[J].计算机仿真,2005,22(8)：144-147.
[62]Hofmann Thomas. Latent semantic models for collaborative filtering [J]. ACM Transactions on Information Systems,2004,22 (1):89-115.
[63]孟宪福 陈莉.基于贝叶斯理论的协同过滤推荐算法[J].计算机应用，2009,29(10)：2733-2735.
[64]李大学谢名亮,赵学斌.基于朴素贝叶斯方法的协同过滤推荐算法[J].计算机应用,2010，30(6)：1523-1526.
[65]傅鹤岗王竹伟.对基于项目的协同过滤推荐系统的改进[J].重庆理工大学学报，2010，24(9)：70-74.
[66]陈健印鉴.基于影响集的协作过滤推荐算法[J].软件学报,2007，18(7)：1685-1694.
[67]Lemire D Maclachlan A.Slope one predictors for online rating-based collaborative filtering[C].The 5th SIAM International Conference on Data Mining.2005,471-476