An new method to collaborative filtering recommendation based on DBN and HMM
Department of Electric and Science, College of Science, Air Force Engineering University, China
2 Department of Urban and Regional Planning, College of Urban and Environmental Sciences, Peking University, China
3 Department of Civil Engineering, Hunan University, China
The main problems of collaborative filtering are initial rating, data sparsity and recommendation in time. A recommendation approach based on HMM model, which creates nearest neighbour set by simulating the user behaviours of web browsing, is a good way to solve the above problems. However, the HMM or model parameters constantly vary with customer's changing preference. When there is a new type of data to join, the HMM can only be discovered by relearn, which will affect real time of recommendation. Therefore a recommendation approach based on DBN and HMM is proposed. The approach will improve real time recommendation, and experiments shows that it has high recommendation quality.
Key words: hidden markov model(HMM) / dynamic bayes network(DBN) / collaborative filtering recommendation
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.