【作者】 杨焱；

【导师】 孙铁利；

【作者基本信息】 东北师范大学 ， 计算机软件与理论， 2005， 硕士

【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。电子商务推荐系统能够直接与用户交互,模拟商店销售人员向用户提供商品推荐,帮助用户找到所需商品,从而顺利完成购买过程。在日趋激烈的竞争环境下,电子商务推荐系统能有效保留用户、防止用户流失,提高电子商务系统的销售。推荐系统在电子商务系统中具有良好的发展和应用前景,逐渐成为电子商务技术的一个重要研究内容,受到越来越多研究者的关注。 电子商务推荐系统在理论和实践中都得到了很大发展。但是随着电子商务系统规模的进一步扩大,电子商务推荐系统也面临一系列挑战。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设计以及体系结构等关键技术进行了较深入的探索和研究,在分析推荐系统中应用中所面临问题的基础上,对现有的解决方法进行详细的阐述。 论文全面介绍电子商务推荐系统及其相关技术,着重分析基于关联规则的推荐和基于用户协同过滤等几个典型的实现技术。针对电子商务推荐系统面临的实时性挑战,本文提出基于项目聚类的协同过滤推荐算法,通过用户对项评分的相似性对项进行聚类,然后选择与目标项相似性最高的若干个聚类作为查询空间搜索目标项的最近邻居。对改进算法进行了详细的理论分析,阐述其可行性;最后,对本文研究进行全面总结,指出存在的不足,展望了未来进一步研究的方向。更多还原

【Abstract】 With the increasing of the Internet and the development of E-Commerce, the structures of E-Commerce web sites become more and more complex. Therefore,it is hard for consumers to find the products and services wanted. To address this issue, recommendation systems have been proposed to suggest products and helpful information for consumers. Recommendation systems can enhance E-Commerce sales by converting browsers into buyers, increasing cross-sell and building loyalty to prevent user losing. Recommendation systems have gradually become an important part in E-Commerce. More and more research papers about recommendation systems of E-Commerce appear in many kinds of conferences and journals. Though recommendation systems of E-Commerce have been very successful in both research and practice, challenging research problems remain. Aimed at the main challenges of recommendation systems of E-Commerce, this thesis studies some key techniques of recommendation systems of E-Commerce. The main research work in this thesis includes the research of recommendation algorithm, and the research on the structure of recommendation systems. The thesis introduces E-commence recommendation systems and the typical technologies that are implemented in them, the recommendation system based on association rule and the collaborative filtering based on user. In large E-Commerce systems, the real-time requirement of recommendation system is hard to be satisfied. To address this issue, we propose a collaborative filtering recommendation algorithm based on item clustering. This approach first clusters items by the users’ rating on items, based on the similarity between target item and cluster centers, the most similar clusters are selected as the search space, in which to search the nearest neighbor of target item. We theoretically analyze the new approach and prove its feasibility. Finally, we summarize the thesis and point out the future research work.更多还原