个性化推荐算法在汽车销售领域的应用研究

发布时间：2019-01-02 07:17

【摘要】：随着电子商务的迅速发展,个性化推荐系统作为一种新的智能信息服务方式,越来越得到用户的亲睐和企业的重视。同时,作为全球第一大汽车消费国,汽车已经成为日常生活中比较普及的交通工具,由于其参数众多,用户迫切需要个性化推荐系统帮助其进行购买决策。如何将个性化推荐系统成功应用于汽车电子商务,是摆在汽车销售商面前的重大理论和实践课题,也是本文研究的目的和要解决问题的重点所在。论文在对个性化推荐系统及其常用推荐算法进行详细介绍和对比的基础上,分别对新、老用户两类不同人群提出了不同的推荐算法和推荐模型：第一,对新用户提出新的混合个性化推荐算法：论文采用用户主观评分矩阵和汽车客观评分矩阵相结合的办法,构建用户客观评分矩阵,以此来缓解用户评分的稀疏性,在此基础上通过用户偏好关系网络抽取用户之间的相似性,并用“专家意见”代替了传统的评分预测,在保证推荐车型满足用户偏好的基础上,推荐性能最高的车型；第二,对老用户提出一种新的个性化推荐模型：针对老用户研究、探讨了用户满意度的影响,提出一个基于用户满意度的模型,通过综合考虑历史信息和浏览信息,使得推荐算法的准确率较普通协同过滤算法更高。
[Abstract]:With the rapid development of electronic commerce, personalized recommendation system, as a new intelligent information service, has been paid more and more attention by users and enterprises. At the same time, as the largest automobile consumer in the world, automobile has become a popular vehicle in daily life. Because of its numerous parameters, users urgently need personalized recommendation system to help them to make purchase decisions. How to successfully apply personalized recommendation system to automobile electronic commerce is an important theoretical and practical subject in front of automobile dealers. It is also the purpose of this paper and the key point to solve the problem. On the basis of the detailed introduction and comparison of the personalized recommendation system and its common recommendation algorithms, this paper presents two different recommendation algorithms and models for the new and old users: first, This paper proposes a new hybrid personalized recommendation algorithm for new users: this paper uses the combination of user subjective score matrix and vehicle objective score matrix to construct user objective score matrix to alleviate the sparsity of user score. On this basis, the similarity between users is extracted through the user preference relationship network, and the "expert opinion" is used to replace the traditional scoring prediction. On the basis of ensuring that the recommended models meet the user preferences, the best models are recommended. Secondly, a new personalized recommendation model for old users is proposed. According to the research of old users, the influence of user satisfaction is discussed, and a model based on user satisfaction is put forward, which considers historical information and browsing information synthetically. The accuracy of recommendation algorithm is higher than that of common collaborative filtering algorithm.
【学位授予单位】：上海应用技术学院
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F426.471;F274

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