Applying Multidimensional Association Rule Mining to Feedback-Based Recommendation Systems

The main characteristic of collaborative filtering is to provide personalized recommendations to a customer based on the customer profile, without considering content information about domain items. In this paper, we investigated to use a relevance feedback mechanism in the collaborative recommendation system. First, we used the Self-organizing Map (SOM) method to avoid suffering from the scalability and sparsity problem in the collaborative filtering. In addition, we adopted the Statistical Attribute Distance (SAD) method which uses the similarity in statistics of customers´ ratings to calculate customer correlations, instead of using the statistics of customers that rate for similar items. Then, the multi-tier granule mining algorithm was used to find association rules. Finally, with the relevance feedback mechanism and the association rules, the recommendations could be refined to provide customers more relevance information.