A Novel Top-K Automobiles Probabilistic Recommendation Model Using User Preference and User Community
Zhuo Chen, Yong Feng, Heng Li · 2014
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.