【作者】 秦岭；

【导师】 乐嘉锦；

【作者基本信息】 东华大学 ， 计算机软件与理论， 2011， 硕士

【摘要】 目前,电子商务领域中的推荐算法已经发展得比较成熟,尤其是协同过滤推荐算法以及由其所衍生出来的各种推荐算法。这些推荐算法主要应用于B2C的电子商务系统中。但是随着应用的不断深入,企业级的(B2B)电子商务系统越来越多,企业级电子商务系统对推荐算法的需求也愈加强烈。传统的协同过滤推荐算法有诸多弊病,在用户首次注册并登陆系统时存在冷启动问题,在某些特殊的领域或面对某些特殊的用户时又存在数据稀疏性问题,基于用户的协同过滤算法的时间复杂度高,对服务器的负荷较大,而基于项目的协同过滤算法虽可线下预先运算,算法效率快,但却需要数据仓库的支持,给许多企业级电子商务系统带来麻烦,而且应用于企业用户的各种算法也在某种程度上有异于应用于B2C的算法。本文设计并实现了一个业内领先的企业级电子商务系统——华贸易货交易所系统,并在对协同过滤等推荐算法进行深入研究,对比多种搜索、排序算法后,为该系统的商品推荐功能提供了一种面向企业用户的在线商品推荐算法,致力于追求高效率、高质量地推荐最热销又最符合客户需求的好商品。本算法利用电子商务系统中普遍存在的商品和企业的多级分类‘结构,快速而准确地确定了企业用户的最近邻居以及待推荐商品所涉及的交易记录,通过这些交易记录对商品进行评分,避免了算法执行过程中遇到的冷启动以及数据稀疏性问题。通过有效建立堆的数据结构,方便高效地根据评分结果对待推荐的商品进行排序,提高了算法的执行效率。由于时代的飞速发展,企业所需要的商品也在不断地更新换代,陈旧的商品显然不能满足企业日益增长的需求,因此本算法抛弃了对一定时间之前的交易记录,仅针对最近的交易记录进行分析,不仅优化了算法效率,更提高了推荐的质量,提高了客户满意度。由于根据交易记录所得出的推荐项可能存在商品已经售完的问题,因此本算法使用Levenshtein Distance(编辑距离)字符串相似性算法寻找商品原发布者重新上架用来代替已售罄商品的新商品,解决了推荐项无效的问题。更多还原

【Abstract】 Currently, the recommendation algorithms in the e-commerce domain have been well developed, especially in collaborative filtering algorithms and the variety of recommendation algorithm derived from it. These recommendation algorithm is mainly used in B2C e-commerce system. But with the developing of applications, the number of business-to-business (B2B) e-commerce systems is highly increased, so the needs of the recommendation algorithm from B2B e-commerce system have become even more intense.The traditional collaborative filtering algorithm has many drawbacks. Cold start problem exists when the user firstly register and log in the system, and data sparseness problem occurs in some specific areas or customers. User-based collaborative filtering algorithm has a high time complexity, high cost on server. Project-based collaborative filtering algorithm can be processed offline, efficiency, low time cost and faster, but it needs the support of data warehousing. These bring a lot of trouble to B2B e-commerce systems. The appliance of recommendation algorithms to corporate users are also somewhat different from the algorithms used in B2C systems.In this paper, we designed and developed a leading B2B e-commerce system in certain domain which is Huamao barter exchange system. After deeply studied the collaborative filtering recommendation algorithm, compared to a variety of searching, sorting algorithm, we provide a online recommendation algorithm for corporate users for that system. The algorithm is committed to have high efficiency, high-quality and recommend the best selling and best products that meet the customer needs. The algorithm takes advantage of multi-level classification structure which is prevalent in e-commerce systems to quickly and accurately determine the nearest neighbor of the enterprise and goods involved in the transaction records. They can be used to mark out the products we want to recommend. The algorithm resolves the sparse data problem and cold-start problem. Through the effective establishment of the heap data structure, we can conveniently and efficiently sort the goods based on score results that successfully improved the efficiency of the algorithm.During the rapid development, the goods needed by businesses are constantly upgrading, the old product obviously does not meet the growing needs of businesses. So the algorithm abandon the transaction record which is long time ago. We only analyze the most recent transaction records, not only increase the efficiency of the algorithm, but also improve the quality of recommendations and customer satisfaction. The recommendation based on of the transaction log may bring a problem that the goods which is recommended may have been sold out. So the algorithm takes advantage of string similarity algorithm based on Levenshtein Distance to find the new product which is a replacement of the original product from the original publisher. That solves the problem of invalid recommendation.更多还原