Hidden Markov model fused with staying time for personalized recommendation

Abstract

Static model in the recommendation system often regards the user's interest as changeless,which is inconsis-tent with the actual to a certain extent.With regards to this,a hidden Markov model fused with staying time for personal-ized recommendation (ctqHMM) based on the HMM dynamic model is proposed.The proposed model employs the transfer of the implicit state variables to simulate the changes of Web users' interests,and uses staying time to describe the level of interest to the specific preference and the importance of the recommended pages.Then,a user's clustering method based on the stationary distribution of the ctqHMM is also proposed and applied into the recommending systems.Experiment results on real Web server access log data show the encouraging performance of the proposed method over the state-of-the-arts.

Keywords

• Web mining;classified time homogeneous hidden Markov model;stationary distribution;user clustering;personalized recommendation;HMM