电子商务推荐系统核心技术研究
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摘要
随着互联网的普及和电子商务的发展,电子商务系统在为用户提供越来越多的选择的同时,其结构也变得更加复杂,用户经常会迷失在大量的商品信息空间中,无法顺利找到自己需要的商品。电子商务推荐系统(Recommender System)直接与用户交互,模拟商店销售人员向用户提供商品推荐,帮助用户找到他们真正所需购买的商品。近年来虽然电子商务推荐系统在理论和实践中都得到了很大发展,然而电子商务推荐系统仍面临着一系列挑战。针对电子商务推荐系统面临的主要问题,本文对电子商务推荐系统中推荐算法和推荐系统体系结构等核心技术进行探讨,主要创新如下:
首先,提出基于关联集合的协同过滤推荐算法。在个性化推荐中,尽管用户评价了某些项目,但这些项目是用户在无意或其他偶然因素影响下评价的,与用户自身偏好并没有多大关联,这往往更符合实际情况。这些不相关的项目相当于噪音数据,往往会干扰协同过滤的效果。为了进一步提高推荐精度,我们提出基于关联集合的协同过滤推荐算法:利用Apriori算法得到频繁项集,取得关联集合,再进行协同过滤,真正的依据用户的偏好信息来进行推荐,从而提高推荐精度。试验结果表明,与传统协同过滤推荐算法想比,基于关联集合的协同过滤的推荐算法可以有效地提高推荐精度。
其次,提出柔性电子商务推荐系统。目前大部分的电子商务推荐系统都是一个单一的工具,只能提供一种推荐策略。在电子商务环境下,商品极其丰富,个性需求多种多样,于是迫切需要更加灵活、实用的推荐策略。为此,本文运用柔性理论对电子商务推荐系统进行分析,提出柔性电子商务推荐系统。该系统通过策略模块去完成推荐需求与实现之间的映射,通过这个映射完成不同的推荐服务。系统的设计遵循构件化的原则,以做到随着策略的改变能够灵活的调整。
With the popularization of Internet and the development of E-Commerce, the structure of E-Commerce web site became more and more complex. This situation made it hard for consummers to find the products and services they wanted. To address this issue, recommendation systems were proposed to suggest products and to provide consumers with information to help them decide which products to purchase. In recent years, althrough recommendation systems in E-Commerce have been very successful in both research and practice, challenging research problems remain. Aimed at these challenges, this thesis explore and reseach some core technologies of recommendation system in E-Commerce, including research of recommendation algorithm and research of arthitecture of recommendation systems in E-Commerce. The main works of this thesis are as follows:
Firstly, associative-sets-based collaborative filtering algorithm is proposed. During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users’real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to sovle this problem. It uses frequent itemsets to get associative sets, and makes recommendations according to users’real preferences, so as to enhance the accuracy of recommending results. Test results show that the new algorithm is more accurate than the traditional.
Secondly, a flexible E-Commerce recommendation system is built. Traditional recommendation system is a sole tool with only one recommendation model. In e-commerce environment, commodities is very rich, personal demands are diversification, E-Commerce systems in different occasions require different types of recommended strategies. For that, we analysis the recommendation system with flexible theory, and proposed a flexible e-commerce recommendation system. It maps the implementation and demand through strategy module, and the whole system would be design as standard parts to adapt to the change of the recommendation strategy.
首先,提出基于关联集合的协同过滤推荐算法。在个性化推荐中,尽管用户评价了某些项目,但这些项目是用户在无意或其他偶然因素影响下评价的,与用户自身偏好并没有多大关联,这往往更符合实际情况。这些不相关的项目相当于噪音数据,往往会干扰协同过滤的效果。为了进一步提高推荐精度,我们提出基于关联集合的协同过滤推荐算法:利用Apriori算法得到频繁项集,取得关联集合,再进行协同过滤,真正的依据用户的偏好信息来进行推荐,从而提高推荐精度。试验结果表明,与传统协同过滤推荐算法想比,基于关联集合的协同过滤的推荐算法可以有效地提高推荐精度。
其次,提出柔性电子商务推荐系统。目前大部分的电子商务推荐系统都是一个单一的工具,只能提供一种推荐策略。在电子商务环境下,商品极其丰富,个性需求多种多样,于是迫切需要更加灵活、实用的推荐策略。为此,本文运用柔性理论对电子商务推荐系统进行分析,提出柔性电子商务推荐系统。该系统通过策略模块去完成推荐需求与实现之间的映射,通过这个映射完成不同的推荐服务。系统的设计遵循构件化的原则,以做到随着策略的改变能够灵活的调整。
With the popularization of Internet and the development of E-Commerce, the structure of E-Commerce web site became more and more complex. This situation made it hard for consummers to find the products and services they wanted. To address this issue, recommendation systems were proposed to suggest products and to provide consumers with information to help them decide which products to purchase. In recent years, althrough recommendation systems in E-Commerce have been very successful in both research and practice, challenging research problems remain. Aimed at these challenges, this thesis explore and reseach some core technologies of recommendation system in E-Commerce, including research of recommendation algorithm and research of arthitecture of recommendation systems in E-Commerce. The main works of this thesis are as follows:
Firstly, associative-sets-based collaborative filtering algorithm is proposed. During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users’real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to sovle this problem. It uses frequent itemsets to get associative sets, and makes recommendations according to users’real preferences, so as to enhance the accuracy of recommending results. Test results show that the new algorithm is more accurate than the traditional.
Secondly, a flexible E-Commerce recommendation system is built. Traditional recommendation system is a sole tool with only one recommendation model. In e-commerce environment, commodities is very rich, personal demands are diversification, E-Commerce systems in different occasions require different types of recommended strategies. For that, we analysis the recommendation system with flexible theory, and proposed a flexible e-commerce recommendation system. It maps the implementation and demand through strategy module, and the whole system would be design as standard parts to adapt to the change of the recommendation strategy.
引文
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6 Dragomir R.Radev,Weiguo Fan,Zhu Zhang. WebInEssence:A Personalized Web-Based Multi-Document Summarization and Recommendation System. http ://www.searchenginewatch.com.
7吴丽花,刘鲁.个性化推荐系统用户建模技术综述.情报学报. 2006.2:55-62.
8 Chuanfeng Chiu,Timothy K.Shih,Yinghong Wang. An Integrated Analysis Strategy and Mobile Agent Framework for Recommendation System in EC over Internet. Tamkang Journal of Science and Engineering, 2002, 5(3):159-174.
9 Kangning Wei,Jinghua Huang,Shaohong Fu. A Survey of E-Commerce Recommender Systems. 2007 International Conference on Service Systems and Service Management, 2007.
10 Sarwar B,Karypis G,Konstan J,et al. Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the Tenth International Conference on World Wide Web.ACM Press, 2001:285-295.
11 Abbattista,F.,Degemmis,M.,and Zambetta,F. Improving the Usability of an E-Commerce Web Site through Personalization. In Proceedings of the Workshop on Recommendation and Personalization in Ecommerce, 2002.
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2 Schafer,J.B.,Konstan,J.A.,and Riedl,J. Recommender Systems in E-Commerce. In ACM Conference on Electronic Commerce(EC99), 1999.
3 Schafer,J.B.,Konstan,J.A.,and Riedl,J. E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery, 2001.
4 Lawrencerd,Almasigs,Kotlyarv,etal. Personalization of Supermarket Product Recommendations. IBMResearchReport, 2000.
5 Cyrus Shahabi,Farnoush Banaei,Kashani. Yoda.An Accurate and Scalable Web-based Recommendation System. Grid and Cooperative Computing:Second International Workshop, 2003.
6 Dragomir R.Radev,Weiguo Fan,Zhu Zhang. WebInEssence:A Personalized Web-Based Multi-Document Summarization and Recommendation System. http ://www.searchenginewatch.com.
7吴丽花,刘鲁.个性化推荐系统用户建模技术综述.情报学报. 2006.2:55-62.
8 Chuanfeng Chiu,Timothy K.Shih,Yinghong Wang. An Integrated Analysis Strategy and Mobile Agent Framework for Recommendation System in EC over Internet. Tamkang Journal of Science and Engineering, 2002, 5(3):159-174.
9 Kangning Wei,Jinghua Huang,Shaohong Fu. A Survey of E-Commerce Recommender Systems. 2007 International Conference on Service Systems and Service Management, 2007.
10 Sarwar B,Karypis G,Konstan J,et al. Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the Tenth International Conference on World Wide Web.ACM Press, 2001:285-295.
11 Abbattista,F.,Degemmis,M.,and Zambetta,F. Improving the Usability of an E-Commerce Web Site through Personalization. In Proceedings of the Workshop on Recommendation and Personalization in Ecommerce, 2002.
12李峰,李军怀,王瑞林,张璟.基于商品特征的个性化推荐算法.计算机工程与应用. 2007, 43(17):24-27.
13曾春,邢春晓,周立柱.基于内容过滤的个性化搜索算法.软件学报. 2003, 14(5):1-7
14唐灿,唐亮贵,刘波.一个面向新兴趣点发现的模糊兴趣挖掘算法.计算机科学.2007, 34(6).
15张海燕,丁峰,姜丽红.基于模糊聚类的协同过滤推荐方法.计算机仿真. 2005, 22(8):44-51
16崔林,宋瀚涛,陆玉昌.基于语义相似性的资源协同过滤技术研究.北京理工大学学报. 2005,25(5):16-19
17 Konstan,J.A.,Miller,B.N.,and Riedl,J. Grouplens:Applying Collaborative Filtering to Usenet News.Communications of the ACM, 1997.
18 Ahn,H.J. Utilizing Popularity Characteristics for Product Recommendation.International Journal of Electronic Commerce, 2006,1(2):59-80.
19 Lorraine,M.,Barry,S. Adaptive selection: an analysis of critiquing and preference-basedfeedback in conversational recommender systems.International Journal of Electronic Commerce, 2006, 11(2): 35-57.
20 Kai Yu,Xiaowei Xu,and Ester,M. Feature Weighting and Instance Selection for Collaborative Filtering:An Information-Theoretic Approach.Knowledge and Information Systems, 2002.
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