电子商务个性化推荐系统研究
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摘要
随着电子商务相关技术的日益完善,越来越多的人们接受了网络购物这一新兴消费方式,但个人相对稳定的购买需求与网站所提供纷繁庞杂商品之间存在矛盾,如何解决这一矛盾成为各国研究人员和电子商务参与者关注的问题。本文主要研究应用个性化技术的推荐系统,着力于探讨如何更好的利用用户有意识或无意识反映出的偏好信息,为个性化推荐原型系统服务。
分析了目前普遍采用的显式和隐式两种用户偏好获取方式的优缺点,提出了一种混合用户偏好获取模式。在客户端利用嵌入浏览器的脚本语言获取用户浏览行为信息,并通过评分转化规则得到大量反映用户偏好的隐式评分,弥补显式评分数据稀疏的缺陷。将隐式评分和相对准确的显式评分作为用户兴趣模型更新的数据来源。同时,针对电子商务参与者兴趣变化异常频繁的特点,提出了基于线性衰减的用户兴趣模型。根据注册信息和浏览行为建立初始兴趣模型,考虑到用户兴趣项目经常变化的特点,构建了链式向量空间模型表示的兴趣模型。兴趣模型中的用户评分在固定时间间隔t进行自然衰减直至变为0而被淘汰,若在衰减过程中产生了新的访问记录和显式反馈,评分将更新为新的评分并继续参与到衰减过程,构成了用户兴趣模型的主要更新过程。
基于协作过滤算法的推荐系统原型对相似度计算、邻居集体积大小和协作过滤推荐算法进行了评估测试,验证了系统的可行性。利用嵌入了特殊脚本的浏览器对用户页面停留时间、鼠标点击次数和页面滚动时间三种可能反映用户偏好的行为进行了相关性测试,结果表明页面停留时间和滚动时间与用户偏好度关联紧密,鼠标点击次数并未表现出明显关联。
With the popularity of e-commerce and related technologies improving, more and more people accept the consumption patterns of net purchases, but the contradiction between the relative stability of personal needs and the numerous and complex goods becomes increasingly sharp, and how to resolve this problem become a hot spots in research. The personalized recommender systems are regarded as the study object. The focus is the ways to better use of preference that the user has consciously or unconsciously reflect, for the personalized recommendation service prototype system.
The advantages and disadvantages of the explicit and implicit user preferences access are analyzed and a mixed-mode preference access pattern is proposed. Users' browsing behavior is collected by the client browser and is converted to the ratings by transformation rules, which can make up for sparse explicit data. The mixed-model combines the implicit and explicit ratings to express user profile. For such extremely frequent changes in user interest, a linear-attenuation based user profile is proposed. The registered information and browsing behaviors are used to set up the initial user profile. Because of the frequent change of user interests, use profile is represented by a chain vector space model. User ratings decrease at a fixed time interval t until the rating has been eliminated to zero. User profile updates rating by a new record of visits and explicit rating, and then continues to be involved in the attenuation process. These constitute a model of user profile in the main update process.
Collaborative filtering based recommendation prototype system test the similarity algorithms, the neighbor set size and collaborative filtering algorithms, which verify the feasibility of the system. The special script embedded browser test the user residence time, mouse clicks and page scrolling time that may reflect user preferences. The test results show that the residence time and scroll the page of time associated with the user preference closer and mouse clicks don't show an obvious correlation.
分析了目前普遍采用的显式和隐式两种用户偏好获取方式的优缺点,提出了一种混合用户偏好获取模式。在客户端利用嵌入浏览器的脚本语言获取用户浏览行为信息,并通过评分转化规则得到大量反映用户偏好的隐式评分,弥补显式评分数据稀疏的缺陷。将隐式评分和相对准确的显式评分作为用户兴趣模型更新的数据来源。同时,针对电子商务参与者兴趣变化异常频繁的特点,提出了基于线性衰减的用户兴趣模型。根据注册信息和浏览行为建立初始兴趣模型,考虑到用户兴趣项目经常变化的特点,构建了链式向量空间模型表示的兴趣模型。兴趣模型中的用户评分在固定时间间隔t进行自然衰减直至变为0而被淘汰,若在衰减过程中产生了新的访问记录和显式反馈,评分将更新为新的评分并继续参与到衰减过程,构成了用户兴趣模型的主要更新过程。
基于协作过滤算法的推荐系统原型对相似度计算、邻居集体积大小和协作过滤推荐算法进行了评估测试,验证了系统的可行性。利用嵌入了特殊脚本的浏览器对用户页面停留时间、鼠标点击次数和页面滚动时间三种可能反映用户偏好的行为进行了相关性测试,结果表明页面停留时间和滚动时间与用户偏好度关联紧密,鼠标点击次数并未表现出明显关联。
With the popularity of e-commerce and related technologies improving, more and more people accept the consumption patterns of net purchases, but the contradiction between the relative stability of personal needs and the numerous and complex goods becomes increasingly sharp, and how to resolve this problem become a hot spots in research. The personalized recommender systems are regarded as the study object. The focus is the ways to better use of preference that the user has consciously or unconsciously reflect, for the personalized recommendation service prototype system.
The advantages and disadvantages of the explicit and implicit user preferences access are analyzed and a mixed-mode preference access pattern is proposed. Users' browsing behavior is collected by the client browser and is converted to the ratings by transformation rules, which can make up for sparse explicit data. The mixed-model combines the implicit and explicit ratings to express user profile. For such extremely frequent changes in user interest, a linear-attenuation based user profile is proposed. The registered information and browsing behaviors are used to set up the initial user profile. Because of the frequent change of user interests, use profile is represented by a chain vector space model. User ratings decrease at a fixed time interval t until the rating has been eliminated to zero. User profile updates rating by a new record of visits and explicit rating, and then continues to be involved in the attenuation process. These constitute a model of user profile in the main update process.
Collaborative filtering based recommendation prototype system test the similarity algorithms, the neighbor set size and collaborative filtering algorithms, which verify the feasibility of the system. The special script embedded browser test the user residence time, mouse clicks and page scrolling time that may reflect user preferences. The test results show that the residence time and scroll the page of time associated with the user preference closer and mouse clicks don't show an obvious correlation.
引文
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[4]艾瑞咨询集团.2007-2011年中国网络购物市场趋势预测报告.http://www.iresearch.com.cn.
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[7]Paul Resnick,Neophytos Iacovou,Mitesh Suchak,Peter Bergstrom,John Riedl.An open architecture for collaborative filtering of netnews.Proceedings of ACM Conference on Computer Supported Cooperative Work,1994:175-186
[8]Upendra Shardanand,Pattie Maes.Social Information Filtering:Algorithms for Automating "Word of Mouth".Proceedings of the SIGCHI conference on Human factors in computing systems,1995:210-217
[9]Shyong K.Lam,John Riedl.Shilling recommender systems for fun and profit.Proceedings of the 13th international conference on World Wide Web,2004:393-402
[10]HPRS A Profitability based Recommender System.Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management,2007:219-223
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[14]Bradley N.Miller,Istvan Albert,Shyong K.Lam,Joseph A.Konstan,John Riedl.MovieLens Unplugged:experiences with an occasionally connected recommender system.Proceedings of the 8th international conference on Intelligent user interfaces,2003:263-266
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[16]Abhinandan Das,Mayur Datar,Ashutosh Garg,Shyam Rajaram.Google News Personalization:Scalable Online Collaborative Filtering.Proceedings of the 16th international conference on World Wide Web,2007:271-280
[17]http://www.mystrands.com/download
[18]http://recsys.acm.org/2008/
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[31]Thomas Tran,Robin Cohen.Hybrid Recommender Systems for Electronic Commerce.AAAI Technical Report WS-00-04,2000:78-84
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[39]赵银春,付关友,朱征宇.基于Web浏览内容和行为相结合的用户兴趣挖掘.计算机工程,2005,31(12):93-94
[40]http://www.amazon.com
[41]韩慧俊.电子商务个性化推荐系统的研究:[硕士学位论文].上海:上海交通大学,2007
[42]曾春,邢春晓,周立柱.个性化服务技术综述.软件学报,2002,13(10):1952-1961
[43]Alexandre Evfimievski,Ramakrishnan Srikant,Rakesh Agrawal,Johannes Gehrke.Privacy Preserving Mining of Association Rules.Information System,2004,29(4):343-364
[44]Xindong Wu,Chengqi Zhang,Shichao Zhang.Efficient mining of both positive and negative association rules.ACM Transactions on Information Systems,2004,22(3):381-405
[45]Badrul Sarwar,George Karypis,Joseph Konstan,John Riedl.Analysis of recommendation algorithms for e-commerce.Proceedings of the 2nd ACM conference on Electronic commerce,2000:158-167.
[46]Yonatan Aumann,Yehuda Lindell.A Statistical Theory for Quantitative Association Rules.Journal of Intelligent Information Systems,2003,20(3):255-283
[47]AWM Smeulders,M Worring,S Santini,A Gupta,R Jain.Content-based image retrieval at the end of the early years.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(12):1349-1380
[48]M Balabanovic,Y Shoham.Fab:content-based,collaborative Recommendation.Communications of the ACM,1997,40(3):66-72
[49]Justin Basilico,Thomas Hofmann.Unifying Collaborative and Content-Based Filtering.Proceedings of the twenty-first international Conference on Machine learning,2004:9
[50]B Sarwar,G Karypis,J Konstan,J Riedl.Analysis of Recommender Algorithms for E-Commerce.Proceedings of the 2nd ACM Conference on Electronic Commerce,2000:158-167
[51]Joseph A.Konstan,John Riedl.Collaborative filtering:supporting social navigation in large,crowded infospaces.Designing information spaces:the social navigation approach,2003,43-82
[52]JL Herlocker,JA Konstan,LG Terveen,JT Riedl.Evaluating Collaborative Filtering Recommender Systems.ACM Transactions on Information System,2004,22(1):5-53
[53]B Sarwar,G Karypis,J Konstan,J Reidl.Item-based Collaborative Filtering Recommendation Algorithms.Proceedings of the 10th international conference on World Wide Web,2001,285-295
[54]邢春晓,高凤荣,战思南等.适应用户兴趣变化的协同过滤推荐算法.计算机研究与发展,2007,44(2):296-301
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[2]Alexander Birukov,Enrico Blanzieri,Paolo Giorgini.Implicit:an agent-based recommendation system for web search.Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems,2005:618-624
[3]余力,刘鲁.电子商务个性化推荐研究.计算机集成制造系统,2004,10(10):1306-1313
[4]艾瑞咨询集团.2007-2011年中国网络购物市场趋势预测报告.http://www.iresearch.com.cn.
[5]J.Ben Schafer,Joseph Konstan,John Riedl.Recommender Systems in E-Commerce.Proceedings of the 1st ACM conference on Electronic Commerce,1999:158-166
[6]J.Ben Schafer,Joseph Konstan,John Riedl.E-Commerce Recommendation Applications.Data Mining and Knowledge Discovery,2001,5(1-2):115-153
[7]Paul Resnick,Neophytos Iacovou,Mitesh Suchak,Peter Bergstrom,John Riedl.An open architecture for collaborative filtering of netnews.Proceedings of ACM Conference on Computer Supported Cooperative Work,1994:175-186
[8]Upendra Shardanand,Pattie Maes.Social Information Filtering:Algorithms for Automating "Word of Mouth".Proceedings of the SIGCHI conference on Human factors in computing systems,1995:210-217
[9]Shyong K.Lam,John Riedl.Shilling recommender systems for fun and profit.Proceedings of the 13th international conference on World Wide Web,2004:393-402
[10]HPRS A Profitability based Recommender System.Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management,2007:219-223
[11]Greg Linden,Brent Smith,Jeremy York.Amazon.com Recommendations:Item-to-Item Collaborative Filtering.IEEE INTERNET COMPUTING,2003, 7(1):76-80
[12]Juha Leino,Kari-Jauko Raiha.Case amazon:ratings and reviews as part of recommendations.Proceedings of the ACM conference on Recommender systems,2007:137-140
[13]Badrul Sarwar,George Karypis,Joseph Konstan,John Riedl.Item-Based Collaborative Filtering Recommendation Algorithms.Proceedings of the 10th international conference on World Wide Web,2001:285-295
[14]Bradley N.Miller,Istvan Albert,Shyong K.Lam,Joseph A.Konstan,John Riedl.MovieLens Unplugged:experiences with an occasionally connected recommender system.Proceedings of the 8th international conference on Intelligent user interfaces,2003:263-266
[15]Michelle Anderson,Marcel Ball,Harold Boley,Stephen Greene,Nancy Howse,Sean McGrath,Daniel Lemire.RACOFI:A Rule-Applying Collaborative Filtering System.International Workshop on Collaboration Agents:Autonomous Agents for Collaborative Environments(COLA'03),2003
[16]Abhinandan Das,Mayur Datar,Ashutosh Garg,Shyam Rajaram.Google News Personalization:Scalable Online Collaborative Filtering.Proceedings of the 16th international conference on World Wide Web,2007:271-280
[17]http://www.mystrands.com/download
[18]http://recsys.acm.org/2008/
[19]邢春晓,高凤荣,战思南等.适应用户兴趣变化的协同过滤推荐算法.计算机研究与发展,2007,44(2):296-301
[20]张光卫,李德毅,李鹏等.基于云模型的协同过滤推荐算法.软件学报,2007,18(10):2403-2411
[21]张守志,许彦.一个个性化服务系统的设计与实现.小型微型计算机,2003,24(12):2155-2158
[22]John Riedl.Recommenders for commerce,content,and community.Proceedings of the 7th IEEE International Conference on E-Commerce Technology,2005
[23]Zhang Fu-guo,Xu Sheng-hua.Topic-level Trust in Recommender Systems.Proceedings of the International Conference on Management Science and Engineering,2007:156-161
[24]D Coldberg,D Nichols,BM Oki,D Terry.Using Collaborative Filtering to Weave an Information Tapestry.Communications of the ACM,1992,35(12):61-70
[25]CLA Hsieh,Justin Zhan,D Zeng,Feiyue Wang.Preserving Privacy in Joining Recommender Systems.Proceedings of the International Conference on Information Security and Assurance,2008:561-566
[26]Paul Resnick,Hal R.Varian.Recommender Systems.Communications of the ACM,1997,40(3):56-58.
[27]Paul Resnick,Rahul Sami.The information cost of manipulation-resistance in recommender systems.Proceedings of the ACM Conference On Recommender System,2008:147-154
[28]M.Benjamin Dias,Dominique Locher,Ming Li,Wael El-Deredy,Paulo J.G.Lisboa.The value of personalised recommender systems to e-business:a case study.Proceedings of the ACM Conference On Recommender System,2008:291-294
[29]David Mcsherry.Explanation in Recommender Systems.Artificial Intelligence Review,2005,24(2):179-197
[30]JA Konstan.Introduction to recommender systems:Algorithms and Evaluation.ACM Transactions on Information Systems,2004,22(1):1-4
[31]Thomas Tran,Robin Cohen.Hybrid Recommender Systems for Electronic Commerce.AAAI Technical Report WS-00-04,2000:78-84
[32]F.Maxwell Harper,Xin Li,Yan Chen,Joseph A.Konstan.An Economic Model of User Rating in an Online Recommender System.Lecture Notes in Computer Science,2005,3538:307-316
[33]G Adomavicius,A Tuzhilin.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions.Knowledge and Data Engineering,2005,17(6):734-749
[34]胡忠望,刘卫东.Cookie应用与个人信息安全研究.计算机应用与软件,2007,24(3):50-53
[35]马亚娜,钱焕延.用Cookie构建Web安全的实现.计算机工程,2002,28(11):34-35
[36]David M.Kristol.HTTP Cookies:Standards,privacy,and politics.ACM Transactions on Internet Technology,2001,1(2):151-198
[37]应晓敏,刘明,窦文华.一种面向个性化服务的无需反例集的用户建模方式.计算机工程与科学,2003,25(6):39-42
[38]Yi Zhang,Jonathan Koren.Efficient Bayesian Hierarchical User Modeling for recommender systems.Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval,2007:47-54
[39]赵银春,付关友,朱征宇.基于Web浏览内容和行为相结合的用户兴趣挖掘.计算机工程,2005,31(12):93-94
[40]http://www.amazon.com
[41]韩慧俊.电子商务个性化推荐系统的研究:[硕士学位论文].上海:上海交通大学,2007
[42]曾春,邢春晓,周立柱.个性化服务技术综述.软件学报,2002,13(10):1952-1961
[43]Alexandre Evfimievski,Ramakrishnan Srikant,Rakesh Agrawal,Johannes Gehrke.Privacy Preserving Mining of Association Rules.Information System,2004,29(4):343-364
[44]Xindong Wu,Chengqi Zhang,Shichao Zhang.Efficient mining of both positive and negative association rules.ACM Transactions on Information Systems,2004,22(3):381-405
[45]Badrul Sarwar,George Karypis,Joseph Konstan,John Riedl.Analysis of recommendation algorithms for e-commerce.Proceedings of the 2nd ACM conference on Electronic commerce,2000:158-167.
[46]Yonatan Aumann,Yehuda Lindell.A Statistical Theory for Quantitative Association Rules.Journal of Intelligent Information Systems,2003,20(3):255-283
[47]AWM Smeulders,M Worring,S Santini,A Gupta,R Jain.Content-based image retrieval at the end of the early years.IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(12):1349-1380
[48]M Balabanovic,Y Shoham.Fab:content-based,collaborative Recommendation.Communications of the ACM,1997,40(3):66-72
[49]Justin Basilico,Thomas Hofmann.Unifying Collaborative and Content-Based Filtering.Proceedings of the twenty-first international Conference on Machine learning,2004:9
[50]B Sarwar,G Karypis,J Konstan,J Riedl.Analysis of Recommender Algorithms for E-Commerce.Proceedings of the 2nd ACM Conference on Electronic Commerce,2000:158-167
[51]Joseph A.Konstan,John Riedl.Collaborative filtering:supporting social navigation in large,crowded infospaces.Designing information spaces:the social navigation approach,2003,43-82
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