Experimental results for considering user dissimilarity in Collaborative Filtering’s Rating Prediction

Dionisis Margaris and Costas Vassilakis
Software and Database Systems (SODA), University of the Peloponnese, 2015 (Technical Report SODA-TR-18001)

Abstract:

In this technical report, we present the experimental findings from applying an algorithm that considers dissimilar users in the rating prediction formulation process, in order to increase coverage in the context of sparse datasets. To this end, the algorithm is applied to five sparse datasets, which are widely used in recommender system research. Additionally, the algorithm is applied to two dense datasets, in order to gain insight on the performance of the proposed technique in this class of datasets.