Improvement of Collaborative Filtering Based on Fuzzy Reasoning Model
Toshihiko Watanabe, Shingo Katayama, Ryosuke Fujioka · 2006
Though various contents are provided through the internet recently, it is not easy to collect favorite contents among huge amounts of contents in terms of user's preference. In this paper, we focus on the collaborative filtering algorithm in the recommender system. We propose a fuzzy modeling approach for preference similarity model in collaborative filtering. In our approach, valid simplified fuzzy model is constructed through optimization of MAE(Mean Absolute Error). The model decides the weights of preference similarity from the value of correlation coefficient and the number of items. Through numerical experiments compared with conventional correlation coefficient based approach using Movie Lens data, the approach is found to be promising for improvement of collaborative filtering model accuracy.