Improvement of Collaborative Filtering Using Evaluation Variance

Abstract

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 modeling approach for preference similarity model in collaborative filtering. In our approach, the 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 and variance values of evaluation ratings of each person. Through numerical experiments compared with conventional correlation coefficient based approach using Movie Lens data, we discuss validness of the model using the evaluation variance.