| Issue |
MATEC Web of Conferences
Volume 44, 2016
2016 International Conference on Electronic, Information and Computer Engineering
|
|
|---|---|---|
| Article Number | 01091 | |
| Number of page(s) | 5 | |
| Section | Computer, Algorithm, Control and Application Engineering | |
| DOI | https://doi.org/10.1051/matecconf/20164401091 | |
| Published online | 08 March 2016 | |
An new method to collaborative filtering recommendation based on DBN and HMM
1
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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