基于人工免疫系统的推荐系统研究
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摘要
信息技术和网络技术的飞速发展，在给人类交流和信息传播带来了革命性变化，为人们的生活、工作和科研带来了巨大方便的同时，也导致了“信息过载”和“信息迷向”。如何帮助人们有效的选择和利用所感兴趣的信息，尽量剔除不相干的信息，使之真正做到“各取所需”，成为信息技术领域的热点问题。推荐系统在此前提下应运而生。经过不到二十年的发展，推荐系统产生了许多新的方法，而最为成熟的技术是协作过滤技术，当然此种技术还是存在着许多不足之处。
人工免疫是近几年在智能技术学科上新兴的研究领域。生物免疫是一个高度复杂的自适应系统，具有学习、记忆和模式识别的能力。通过模拟和应用免疫系统的信息处理能力，可以解决许多科学和工程问题。
本文利用免疫的有关概念和理论，将之与推荐系统结合，提出了基于人工免疫思想的推荐算法。首先对推荐系统中常用技术进行了详细地阐述，指出这些技术的适用范围和优缺点。然后说明了人工免疫系统的生物学基础以及生物免疫一些基本概念，将免疫思想用于协同过滤技术中，提出了一个基于人工免疫系统的协同过滤算法，有效地解决传统协同过滤算法中出现的推荐准确度不够的问题，在计算推荐结果的过程中，提出了新预测评分方法，进一步提高了推荐结果的推荐质量，经改进后的算法在用户兴趣度预测的结果上更客观、更准确，从而提高了系统推荐的质量。最后实验通过从公共数据库中的收集数据来验证了算法的有效性及可行性。
The rapid development of information technology and network technology brings about a revolutionary change for people to live, work and research. This change brought tremendous convenience, also led to the "information overload" and "mislead". It has become a hot issue in the field of information technology that how to help people interested in the effective use of information. Recommendation System came into being in this premise. After less than 20 years of development, recommendation system has produced many methods. Collaborative filtering technology is the most mature technology, of course, there are still many such technical deficiencies.
Artificial Immune is an increasingly important area in the field of computational technology. Biological Immune is a strong information-processing system with diversity-recognizing capability, enhanced learning mechanics and distributed associate memory. Simulating biological immune system, the new computational techniques can solve not only the science but also the engineering problems.
Based on the immune principles, this paper presents propose a modified recommendation algorithm based on artificial immune system. At the beginning, the recommendation system common technologies are introduced. Then the biology of the immune system and the artificial immune some basic concepts are described. Bring in the ideas to the recommendation system and propose a modified recommendation algorithm based on artificial immune system. This algorithm solves the problem of recommendation accuracy by the traditional collaborative filtering algorithm. In the process of the result calculating recommendation, this essay has brought new methods which having improved recommendation result further. At the end, the experiment to collect data from public databases verified the effectiveness and feasibility of the algorithm.
人工免疫是近几年在智能技术学科上新兴的研究领域。生物免疫是一个高度复杂的自适应系统，具有学习、记忆和模式识别的能力。通过模拟和应用免疫系统的信息处理能力，可以解决许多科学和工程问题。
本文利用免疫的有关概念和理论，将之与推荐系统结合，提出了基于人工免疫思想的推荐算法。首先对推荐系统中常用技术进行了详细地阐述，指出这些技术的适用范围和优缺点。然后说明了人工免疫系统的生物学基础以及生物免疫一些基本概念，将免疫思想用于协同过滤技术中，提出了一个基于人工免疫系统的协同过滤算法，有效地解决传统协同过滤算法中出现的推荐准确度不够的问题，在计算推荐结果的过程中，提出了新预测评分方法，进一步提高了推荐结果的推荐质量，经改进后的算法在用户兴趣度预测的结果上更客观、更准确，从而提高了系统推荐的质量。最后实验通过从公共数据库中的收集数据来验证了算法的有效性及可行性。
The rapid development of information technology and network technology brings about a revolutionary change for people to live, work and research. This change brought tremendous convenience, also led to the "information overload" and "mislead". It has become a hot issue in the field of information technology that how to help people interested in the effective use of information. Recommendation System came into being in this premise. After less than 20 years of development, recommendation system has produced many methods. Collaborative filtering technology is the most mature technology, of course, there are still many such technical deficiencies.
Artificial Immune is an increasingly important area in the field of computational technology. Biological Immune is a strong information-processing system with diversity-recognizing capability, enhanced learning mechanics and distributed associate memory. Simulating biological immune system, the new computational techniques can solve not only the science but also the engineering problems.
Based on the immune principles, this paper presents propose a modified recommendation algorithm based on artificial immune system. At the beginning, the recommendation system common technologies are introduced. Then the biology of the immune system and the artificial immune some basic concepts are described. Bring in the ideas to the recommendation system and propose a modified recommendation algorithm based on artificial immune system. This algorithm solves the problem of recommendation accuracy by the traditional collaborative filtering algorithm. In the process of the result calculating recommendation, this essay has brought new methods which having improved recommendation result further. At the end, the experiment to collect data from public databases verified the effectiveness and feasibility of the algorithm.
引文
[1] David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry.Using Colaborative Filtering to Weave an Information Tapestry.December 1992Nol.35, No.12 COMMUNICATIONS OF THE ACM, 61-70
[2] Paul Resnick and Hal R. Varian. Recommender Systems. March 1997Nol. 40, No. 3 COMMUNICATIONS OF THE ACM, 56-58
[3] J. Ben Schafer, Joseph Konstan, and John Riedl. Recommender Systems in E-Commerce.
[4]曾春,邢春晓,周立柱(2002)．个性化服务技术综述．软件学报,2002年,第13卷第10 期．
[5] Mladenic, D. Machine learning for better Web browsing. In: Rogers, S., Iba, W.,eds. AAAI 2000 Spring Symposium Technical Reports on Adaptive User Interfaces. Menlo Park, CA: AAAI Press, 2000.82-84
[6] Pazzani, M.J., Muramatsu, J., Billsus, D. SyskiII& Webert: identifying interesting Web sites. In: Weld, D., Clancey, B.,. 山 .Proceedings of the 13th National Conference on Artificial Inteligence 'and 8th innovative Applications. of Artificial Intelligence Conference. Menlo Park, CA: AAAI Press, 1996.54-61
[7] Lieberman, H. Letizia: an agent that assists web browsing. In: Burke, R., ed. Proceedings of the International Joint Conference on Artificial Inteligence.Menlo Park, CA: AAAI Press, 1995. 924-929
[8] Bollacker, K.D., Lawrence, S., Giles, C.L. Discovering relevant scientific literature on the Web. IEEE Intelligent Systems, 2000,15(2):42-47
[9] Mobasher, B., Cooley.R, Srivastava, J. Automatic personalization based on Web usage mining. Communications of the ACM, 2000, 43(8):142-151
[10] Rich, E., User Modeling via Stereotypes, Cognitive Science 3, 1979, pp. 329-354
[11] Krulwich, B., Lifestyle Finder: Intelligent User Profiling Using LargeScale Demographic Data,Artificial Intelligence Magazine, 1997, 18 (2): 37-45
[12] Pazzani, M. J., A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Review, 1999, 13 (5/6): 393-408
[13] John s. Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th conference on Uncertainly in Artificial Intelligence, pages 43-52, 1998.
[14] Billsus, D&Pazzani, M. Learning Collaborative Information Filters, in Proceedings of the International Conference on Machine Learning (Madison WI, July 1998), Morgan Kaufmann Publishers.
[15] Cohen,W.W., Basu,C, Hirsh,H., 1998. Using Social and Content-Based Information in Recommendation. Proceedings of the AAAI-98.
[16] Schafer, J. B., Konstan, J. and Riedl, J, Recommender System in E-Commerce,The first ACM conference on Electronic Commerce, Denver, CO, 1999, November 3-5: 158-166
[17] T.Joachims, D.Freitag, T.Mitchell. Web Watcher: A Tour Guide for the World Wide Web.M.P.Georgeff, E.M.Pollack. Proceedings of the International Joint Conference on Artificial Intelligence. San Francisco, Morgan Kaufmann Publishers, 1997:770-777
[18] J.Rucker, M.J.Polanco. Siteseer: Personalized Navigation for the Web. Communications of the ACM, 1997,40(3):73-75
[19] Application of Dimensionality Reduction in Recommender System-A Case Study.
[20] Schafer, J.B.,Konstan,J.,and Riedl,J.(1999).Recommender Systems in E-Commerce. In Proceedings of ACM E-Commerce 1999 conference.
[21] 杨峰，战守义，沈福祥．P2P超级点网络可靠性及动态自恢复机制的研究。计算机应用，2004．
[22] Shalendra Chhabra,Emesto Damiani,Sabrina De Capitani di Vimerciati. A Protocol for Reputation Management in Super-Peer Networks,IEEE.
[23] Shardanand,U.,and Maes,P.(1995).Social Information Filtering:Algorithms for Automating 'World of Mouth'.In Proceedings of CHI '95.Denver, CO.
[24] Hill,W.,Stead,L.,Rosenstein,M.,and Furnas,G.(1995).Recommending and Evaluating Choices in a Virtual Community of Use. In Proceedings of CHI '95
[25] Breese, J. S., Heckerman, D. and Kadie, C., Empirical analysis of predictive algorithms forcollaborative filtering, The 14th Annual Conference on Uncertainty in Artificial Intelligence, 1998, pp. 43-52
[26] Aggarwal, C. C., Wolf, J. L., Wu K., and Yu, P. S., Horting Hatches an Egg: A NewGraphtheoretic Approach to Collaborative Filtering, The ACM KDD'99 Conference. SanDiego, CA, 1999, pp. 201-212
[27] Agrawal, R., Imielinski, T., Swami, A., Fast Alogrithms formMining Association Rules, The 20th VLDB Conference, 1994, pp. 487-499
[28] Park, J., Chen, M., Yu, P., An Effective Hash-based Algorithm for Mining Association Rules, ACM SIGMODConf. On Management of Data, 1995
[29] Agarwal, R.C., Aggarwal, C., Prasad, V.V.V., A Tree Projection Algorithm for Generation of Frequent Itemsets,Journal of Parallel and Distributed Computin, 2000.
[30] Han, J., Pei, J., Yin, Y., Mining Frequent Patterns Without Candidate Generation, Technical Report CMPT99-12,School of Computin Science, Simon Fraser University, 1999.
[31] Fisher D, Hildrum K, Hong J, Newman M and Vuduc R, SWAMI: a framework for CF algorithm development and evaluation 1999.Http://guir.berkeley.edu/projects/swami/.
[32] 于善谦，王洪海，朱乃硕，叶荣．免疫学导论．北京：高等教育出版社，施普林格出版社．1999．
[33] Burnet F M. The Clonal Selection Theory of Acquired Immunity[M].London:Cambridge University Press, 1959
[34] Jerne NK, Towards a network theory of the immune system Annals of Immunology, vol.125.no.C,pp. 373-389, 1973
[35] 李涛．计算机免疫学．北京：电子工业出版社，2004．
[36] 焦李成，杜海峰。人工免疫系统进展与展望。电子学报，2003，31(10)
[37] De.Castro L N.&Von Zuben F. J. Artificial Immune System: Part Ⅰ-Basic Theory and Applicarion[A].Technical Report-RT DCA01,1999
[38] Dasgupta D.&Attoh O. N. Immunity Based System: A Survey [C]. Proceedings of IEEE Internatioal Conference on Systems, Man, and Cybernetics, Orlando, Florida, 1997, 369-374
[39] Hunt J. E.&Cooke D. E. Learning Using an Artificial Immune System[J].Journal of Network and Computer Applications. 1996,19(2): 189-212
[40] Timmis J,Neal M.A resoure limited artificial immune system for data analysis[J].Knowledge Based Systems, 2001,14(3-4): 121-130
[41] De Castro L N, Von Zuben F J. An evolutionary immune network for data clustering[C]. Proceedings of the IEEE SBRN'00(Brazilian Symposium on Artificial Neural NetWorks),2000
[42] 梁刚，基于免疫机制的入侵检侧系统电子科技大学硕士学位论文，2004：35-42
[43] Leandro Nunes de Castro, Fernando Jose Von Zuben. The Clonal Selection Algorithm with Engineering Applications. Workshop on Artificial Immune Systems and Their Applications, Las Vegas, USA, In Workshop Proceedings of GECC'00, July 2000.7, 36-37
[44] Kazuyuki Mori, Makoto Tsukiyama, Toyoo Fukuda. Adaptive scheduling system inspired by immune system, 1998 IEEE International Conference on Systems, Man, and Cybernetics 3833—3837
[45] 周明，孙树栋．遗传算法原理及应用．国防工业出版社，1999，7，1-175
[46] 曹先彬，刘克胜，王煦法．基于免疫遗传算法的装箱问题求解．小型微型计算系统，2000，Vol．21N04：362-363
[47] 张著洪，黄席械，一种新的免疫算法及其在多模态函数优化中的应用控制理论与应用，2003．2．Vol．21 No．1：1721
[48] 王凌，郑大钟．多目标优化的一类模拟退火算法．计算机工程与应用，2002，Vol．38 No 8：4-5
[49] De Castro L. N., Von Zuben F. J.. Artificial Immune Systems: Part Ⅱ—A Survey of Applications. Technical Report, DCA-RT 02/00, 2000.2
[50] 李涛．计算机免疫学．北京：电子工业出版社，2004．
[51] H.Bersini and F.J.Varela. The immune recruitment mechanism: A selective evolutionary strategy. Proceedings of the Fourth International Conference on Genetic Algorithms. San Diego. July, 1991:520-526
[52] C.J.Gibert and T. W. Routen. Associative Memory in an Immune-based System. Proceedings of the 12th National Conference on Artificial Intelligence(AAAI-94). Seattle. July 31-August 4, 1994:852-857
[53] 莫宏伟，金鸿章．免疫算法原理与应用．航空计算技术，2002，32(4)：49-51
[54] Farmer JD, Parkard NH and Perelson AS, The immune system, adaptation, and machine learning Physical, vol.22.pp. 187-204,1986.
[55] http://www.moviecritic.com/
[2] Paul Resnick and Hal R. Varian. Recommender Systems. March 1997Nol. 40, No. 3 COMMUNICATIONS OF THE ACM, 56-58
[3] J. Ben Schafer, Joseph Konstan, and John Riedl. Recommender Systems in E-Commerce.
[4]曾春,邢春晓,周立柱(2002)．个性化服务技术综述．软件学报,2002年,第13卷第10 期．
[5] Mladenic, D. Machine learning for better Web browsing. In: Rogers, S., Iba, W.,eds. AAAI 2000 Spring Symposium Technical Reports on Adaptive User Interfaces. Menlo Park, CA: AAAI Press, 2000.82-84
[6] Pazzani, M.J., Muramatsu, J., Billsus, D. SyskiII& Webert: identifying interesting Web sites. In: Weld, D., Clancey, B.,. 山 .Proceedings of the 13th National Conference on Artificial Inteligence 'and 8th innovative Applications. of Artificial Intelligence Conference. Menlo Park, CA: AAAI Press, 1996.54-61
[7] Lieberman, H. Letizia: an agent that assists web browsing. In: Burke, R., ed. Proceedings of the International Joint Conference on Artificial Inteligence.Menlo Park, CA: AAAI Press, 1995. 924-929
[8] Bollacker, K.D., Lawrence, S., Giles, C.L. Discovering relevant scientific literature on the Web. IEEE Intelligent Systems, 2000,15(2):42-47
[9] Mobasher, B., Cooley.R, Srivastava, J. Automatic personalization based on Web usage mining. Communications of the ACM, 2000, 43(8):142-151
[10] Rich, E., User Modeling via Stereotypes, Cognitive Science 3, 1979, pp. 329-354
[11] Krulwich, B., Lifestyle Finder: Intelligent User Profiling Using LargeScale Demographic Data,Artificial Intelligence Magazine, 1997, 18 (2): 37-45
[12] Pazzani, M. J., A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Review, 1999, 13 (5/6): 393-408
[13] John s. Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th conference on Uncertainly in Artificial Intelligence, pages 43-52, 1998.
[14] Billsus, D&Pazzani, M. Learning Collaborative Information Filters, in Proceedings of the International Conference on Machine Learning (Madison WI, July 1998), Morgan Kaufmann Publishers.
[15] Cohen,W.W., Basu,C, Hirsh,H., 1998. Using Social and Content-Based Information in Recommendation. Proceedings of the AAAI-98.
[16] Schafer, J. B., Konstan, J. and Riedl, J, Recommender System in E-Commerce,The first ACM conference on Electronic Commerce, Denver, CO, 1999, November 3-5: 158-166
[17] T.Joachims, D.Freitag, T.Mitchell. Web Watcher: A Tour Guide for the World Wide Web.M.P.Georgeff, E.M.Pollack. Proceedings of the International Joint Conference on Artificial Intelligence. San Francisco, Morgan Kaufmann Publishers, 1997:770-777
[18] J.Rucker, M.J.Polanco. Siteseer: Personalized Navigation for the Web. Communications of the ACM, 1997,40(3):73-75
[19] Application of Dimensionality Reduction in Recommender System-A Case Study.
[20] Schafer, J.B.,Konstan,J.,and Riedl,J.(1999).Recommender Systems in E-Commerce. In Proceedings of ACM E-Commerce 1999 conference.
[21] 杨峰，战守义，沈福祥．P2P超级点网络可靠性及动态自恢复机制的研究。计算机应用，2004．
[22] Shalendra Chhabra,Emesto Damiani,Sabrina De Capitani di Vimerciati. A Protocol for Reputation Management in Super-Peer Networks,IEEE.
[23] Shardanand,U.,and Maes,P.(1995).Social Information Filtering:Algorithms for Automating 'World of Mouth'.In Proceedings of CHI '95.Denver, CO.
[24] Hill,W.,Stead,L.,Rosenstein,M.,and Furnas,G.(1995).Recommending and Evaluating Choices in a Virtual Community of Use. In Proceedings of CHI '95
[25] Breese, J. S., Heckerman, D. and Kadie, C., Empirical analysis of predictive algorithms forcollaborative filtering, The 14th Annual Conference on Uncertainty in Artificial Intelligence, 1998, pp. 43-52
[26] Aggarwal, C. C., Wolf, J. L., Wu K., and Yu, P. S., Horting Hatches an Egg: A NewGraphtheoretic Approach to Collaborative Filtering, The ACM KDD'99 Conference. SanDiego, CA, 1999, pp. 201-212
[27] Agrawal, R., Imielinski, T., Swami, A., Fast Alogrithms formMining Association Rules, The 20th VLDB Conference, 1994, pp. 487-499
[28] Park, J., Chen, M., Yu, P., An Effective Hash-based Algorithm for Mining Association Rules, ACM SIGMODConf. On Management of Data, 1995
[29] Agarwal, R.C., Aggarwal, C., Prasad, V.V.V., A Tree Projection Algorithm for Generation of Frequent Itemsets,Journal of Parallel and Distributed Computin, 2000.
[30] Han, J., Pei, J., Yin, Y., Mining Frequent Patterns Without Candidate Generation, Technical Report CMPT99-12,School of Computin Science, Simon Fraser University, 1999.
[31] Fisher D, Hildrum K, Hong J, Newman M and Vuduc R, SWAMI: a framework for CF algorithm development and evaluation 1999.Http://guir.berkeley.edu/projects/swami/.
[32] 于善谦，王洪海，朱乃硕，叶荣．免疫学导论．北京：高等教育出版社，施普林格出版社．1999．
[33] Burnet F M. The Clonal Selection Theory of Acquired Immunity[M].London:Cambridge University Press, 1959
[34] Jerne NK, Towards a network theory of the immune system Annals of Immunology, vol.125.no.C,pp. 373-389, 1973
[35] 李涛．计算机免疫学．北京：电子工业出版社，2004．
[36] 焦李成，杜海峰。人工免疫系统进展与展望。电子学报，2003，31(10)
[37] De.Castro L N.&Von Zuben F. J. Artificial Immune System: Part Ⅰ-Basic Theory and Applicarion[A].Technical Report-RT DCA01,1999
[38] Dasgupta D.&Attoh O. N. Immunity Based System: A Survey [C]. Proceedings of IEEE Internatioal Conference on Systems, Man, and Cybernetics, Orlando, Florida, 1997, 369-374
[39] Hunt J. E.&Cooke D. E. Learning Using an Artificial Immune System[J].Journal of Network and Computer Applications. 1996,19(2): 189-212
[40] Timmis J,Neal M.A resoure limited artificial immune system for data analysis[J].Knowledge Based Systems, 2001,14(3-4): 121-130
[41] De Castro L N, Von Zuben F J. An evolutionary immune network for data clustering[C]. Proceedings of the IEEE SBRN'00(Brazilian Symposium on Artificial Neural NetWorks),2000
[42] 梁刚，基于免疫机制的入侵检侧系统电子科技大学硕士学位论文，2004：35-42
[43] Leandro Nunes de Castro, Fernando Jose Von Zuben. The Clonal Selection Algorithm with Engineering Applications. Workshop on Artificial Immune Systems and Their Applications, Las Vegas, USA, In Workshop Proceedings of GECC'00, July 2000.7, 36-37
[44] Kazuyuki Mori, Makoto Tsukiyama, Toyoo Fukuda. Adaptive scheduling system inspired by immune system, 1998 IEEE International Conference on Systems, Man, and Cybernetics 3833—3837
[45] 周明，孙树栋．遗传算法原理及应用．国防工业出版社，1999，7，1-175
[46] 曹先彬，刘克胜，王煦法．基于免疫遗传算法的装箱问题求解．小型微型计算系统，2000，Vol．21N04：362-363
[47] 张著洪，黄席械，一种新的免疫算法及其在多模态函数优化中的应用控制理论与应用，2003．2．Vol．21 No．1：1721
[48] 王凌，郑大钟．多目标优化的一类模拟退火算法．计算机工程与应用，2002，Vol．38 No 8：4-5
[49] De Castro L. N., Von Zuben F. J.. Artificial Immune Systems: Part Ⅱ—A Survey of Applications. Technical Report, DCA-RT 02/00, 2000.2
[50] 李涛．计算机免疫学．北京：电子工业出版社，2004．
[51] H.Bersini and F.J.Varela. The immune recruitment mechanism: A selective evolutionary strategy. Proceedings of the Fourth International Conference on Genetic Algorithms. San Diego. July, 1991:520-526
[52] C.J.Gibert and T. W. Routen. Associative Memory in an Immune-based System. Proceedings of the 12th National Conference on Artificial Intelligence(AAAI-94). Seattle. July 31-August 4, 1994:852-857
[53] 莫宏伟，金鸿章．免疫算法原理与应用．航空计算技术，2002，32(4)：49-51
[54] Farmer JD, Parkard NH and Perelson AS, The immune system, adaptation, and machine learning Physical, vol.22.pp. 187-204,1986.
[55] http://www.moviecritic.com/