Exploiting social trust via weighted voting strategy for recommendation systems improvement

Abstract

Recently, social trust information has become a significant additional factor in obtaining high-quality recommendations. It has also helped to alleviate the problems of collaborative filtering. In this paper, we exploit explicit and implicit trust relations and incorporate them to take advantage of more ratings (as they exist) of trusted neighbors to mitigate the sparsity issue. We further apply the idea of weighted voting of the ensemble classifier for the election of the most appropriate trust neighbors’ ratings. Additionally, the certainty of these elected rating values was confirmed by calculating their reliability using a modified version of Pearson’s Correlation Coefficient. Finally, we applied the K-Nearest Neighbors method with a linear combination of original and trust-elected ratings using a contribution weight to obtain the best prediction value. Extensive experiments were conducted on two real-world datasets to show that our proposed approach outperformed all comparable algorithms in terms of both coverage and accuracy. Specifically, the improvement ratio ranged approximately from 4% as a minimum to 20% on FilmTrust, and to 10% on Epinions as a maximum in terms of Fmeasure between the inverse of Mean Absolute Error (accuracy) and coverage.