Collaborative Filtering Recommender Model In The Automotive E-commerce Applications

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.