Improving user similarity estimation by incorporating distinct opinions in collaborative filtering recommender systems

Abstract

As the media content industry is growing continuously, the content market has become very competitive. Various strategies such as advertising and Word-of-Mouth (WOM) have been used to draw people’s attention. It is hard for users to be completely free of others’ influences and thus to some extent their opinions become affected and biased. In the field of recommender systems, prior research on biased opinions has attempted to reduce and isolate the effects of external influences in recommendations. In this paper, we present a new measure to detect opinions that are distinct from the mainstream. This distinctness enables us to reduce biases formed by the majority and thus, to potentially increase the performance of recommendation results. To ensure robustness, we develop four new hybrid methods that are various mixtures of existing collaborative filtering (CF) methods and our new measures of Distinctness and Time-aware Distinctness. In this way, the proposed methods can reflect the majority of opinions while considering distinct user opinions. We evaluate the methods using a real-life rating dataset with 5-fold cross validation. The experimental results clearly show that the proposed models outperform existing CF methods.