Title :
A Novel Top-K Automobiles Probabilistic Recommendation Model Using User Preference and User Community
Author :
Zhuo Chen ; Yong Feng ; Heng Li
Author_Institution :
Key Lab. of Dependable Service Comput. in Cyber Phys. Soc., Chongqing Univ., Chongqing, China
Abstract :
The economic level rising and the rapid development of automobile industry offers various opinions for buyers. The automobile dealers provide services to users by recommending popular and fashionable automobiles. However, few of them offer the personalized recommendation services online. In this paper, we propose an automobile recommendation system to recommend top-K ranked automobiles for users. We first propose a model to analyze the individual requirements and user community which may affect the user´s purchase behaviors. And then we develop an estimation algorithm to compute the parameters in proposed model. With the trained model, we conduct the personalized recommendation algorithm using model parameters. Experiments with real-life data sets confirms the effectiveness and scalability of our algorithms.
Keywords :
automobile industry; collaborative filtering; consumer behaviour; purchasing; recommender systems; automobile dealers; automobile recommendation system; estimation algorithm; personalized recommendation services; scalability; suitable automobile industry; top-k automobile probabilistic recommendation model; user community; user purchase behaviors; Automobiles; Automotive engineering; Communities; Computational modeling; Image color analysis; Probabilistic logic; Vectors; Collaborative Filtering; Probabilistic Model; Recommendation System; User Community; User Preference;
Conference_Titel :
e-Business Engineering (ICEBE), 2014 IEEE 11th International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4799-6562-5
DOI :
10.1109/ICEBE.2014.28
