协同过滤推荐模型及其在汽车电子商务中的应用研究

【作者】 张晓蕾；

【导师】 高琳琦；

【作者基本信息】 天津师范大学 ， 管理科学与工程， 2010， 硕士

【摘要】 随着Internet和信息技术的飞速发展,电子商务个性化推荐作为一种崭新的智能信息服务方式,针对不同的用户,通过对用户个性、习惯、偏好的分析,准确地向用户提供感兴趣的信息和服务,满足用户对于个性化产品需求的同时,提高了企业的竞争能力,得到了广泛重视。在大众消费能力逐渐提高、选择产品变得更为精细的今天,汽车作为日常生活中越来越普及的交通工具,人们对其个性化的需求越来越大。由于汽车配置属性参数繁多,其用户群体更需要个性化推荐服务帮助其进行购买决策,个性化推荐系统成为解决这一问题的有效工具。本文在借鉴国内外研究成果的基础上,通过分析汽车电子商务的特点,对协同过滤推荐算法及其在汽车电子商务中的应用进行了研究,主要工作包括：(1)分别从汽车销售商和用户的角度,分析汽车电子商务个性化推荐服务的重要性,并对电子商务个性化推荐系统的组成结构和整体框架,以及主要的推荐技术进行了比较、分析。(2)针对传统协同过滤推荐算法评分数据稀疏性和冷开始问题,采用基于属性值偏好矩阵的协同过滤算法,通过对矩阵的降维以及构建属性值偏好矩阵进行相似性度量,有效缓解稀疏性；同时建立用户反馈机制,降低冷启动问题的发生概率。(3)将上述协同过滤算法应用到汽车电子商务的个性化推荐服务中,构造了汽车电子商务推荐系统模型,设计开发了原型系统,提供了实际应用的基础。更多还原

【Abstract】 With the rapid development of Internet and information technology, e-commerce personalized recommendation as a kind of new way of intelligent information services provides the information and services that the users interested in exactly for different users through analyzing the users’ personality, habits and preferences. It satisfies the users’ demand for personalized products greatly and improves the competitiveness of enterprises.Nowadays, the public consumption ability gradually increases and their products chosen become more and more sophisticated. Meanwhile, car, as the most popular vehicle in daily life, people’s individual demands are growing towards it. Due to the parameter of the car configuration attributes are various, the consumer especially need personalized recommendation services to help them make purchasing decision. Therefore, the recommendation services system is the most effective tool to solve the problem.Based on the research accomplishment both domestic and overseas and analyzing the characteristics of automotive e-commerce, the paper studied on the collaborative filtering algorithm and its application in automotive e-commerce, mainly include:1) The paper analyzed the necessity of automotive e-commerce personalized recommendation service from the vendor and users, compared and analyzed the architecture, the overall framework of e-commerce personalized recommendation system and the main recommendation technology.(2) In accordance with the problems of score sparsity and cold-start of traditional collaborative filtering recommendation algorithm, the paper adopted the collaborative filtering algorithm based on attribute value preference matrix. Through matrix dimensionality and building the attribute value preference matrix to measure similarity, sparsity has been alleviated effectively; while establishing user feedback mechanism reduce cold start.(3) The paper put the collaborative filtering algorithm into the automotive e-commerce personalized recommendation services, and build a model of automotive e-commerce recommendation, designed and developed a prototype system. It provides a basis for practical application.更多还原