Approach to collaborative filtering recommendation based on HMM
基于HMM模型的协同过滤推荐方法

Considering that browse route, browse time, browse times and so on are the important factors to influence the accuracy of commendation, a dynamic collaboration filtering recommendation method based on Hidden Markov Model (HMM) was proposed. First, it simulated users' behaviors while a user was browsing Web pages, and set up the nearest-neighbor set according to his behaviors. Because the data it used was not users' rating but users' browse route, the problem of data sparseness and initial rating was resolved. When HMM was used to replace the similitude model to measure users' similarity, the accuracy of nearest-neighbor commendation was improved greatly. And it settled the on-time recommendation problem and the extensible data space problem. Then the concept of fancy degree was set up, which made the recommendation become more suitable. Finally, the fancy degree was applied to establish the prediction model of dynamic collaboration filtering recommendation. A case study shows the excellent performance of this model.