Improving the Prediction Accuracy of Multicriteria Collaborative Filtering by Combination Algorithms

Abstract

This study focuses on developing the multicriteria collaborative filtering algorithmfor improving the prediction accuracy. The approaches applied were user-item multirating matrix decomposition, the measurement of user similarity using cosine formula and multidimensional distance, individual criteria weight calculation, and rating prediction for the overall criteria by a combination approach. Results of the study show variation in multicriteria collaborative filtering algorithm, which was used for improving the document recommender system with the two following characteristics. First, the rating prediction for four individual criteria using collaborative filtering algorithm by a cosine-based user similarity and a multidimensional distance-based user similarity. Second, the rating prediction for the overall criteria using a combination algorithms. Based on the results of testing, it can be concluded that a variety of models developed for the multicriteria collaborative filtering systems had much better prediction accuracy than for the classic collaborative filtering, which was characterized by the increasingly smaller values of Mean Absolute Error. The best accuracy was achieved by the multicriteria collaborative filtering system with multidimensional distance-based similarity.