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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 9535808, 10 pages
Research Article

A Probability-Based Hybrid User Model for Recommendation System

1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
2Beijing Institute of Astronautic System Engineering, Beijing 310027, China

Received 13 July 2015; Revised 16 December 2015; Accepted 20 December 2015

Academic Editor: Matteo Gaeta

Copyright © 2016 Jia Hao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.