An new method to collaborative filtering recommendation based on DBN and HMM

Abstract

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.

Keywords

• hidden markov model(HMM)
• dynamic bayes network(DBN)
• collaborative filtering recommendation