Enhancing Social Recommendation with Sentiment Communities

Among the various recommender systems proposed in the literature, there is an increase in relevance and number of those that suggest users of possible interest to the target user. In this article, we propose a new algorithm for realizing user recommenders, named SCORES (Sentiment Communities Recommender System). This algorithm relies on the identification of sentiment communities in which, for each topic cited by the user, we consider not only the relative sentiment, but also the volume and the objectivity of contents generated by him. The graph related to each topic is obtained by considering the Tanimoto similarity between users. The recommendation process occurs by clustering the obtained graph to detect latent communities, and suggesting to the target user the most similar K users based on tie strength measures. A comparative analysis between SCORES and some state-of-the-art approaches shows the benefits in term of performance.