MATEC Web of Conferences
Volume 44, 20162016 International Conference on Electronic, Information and Computer Engineering
|Number of page(s)||5|
|Section||Computer, Algorithm, Control and Application Engineering|
|Published online||08 March 2016|
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
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