Dissimilarity recommender

Collaborative recommenders rely on the assumption that similar users may exhibit similar tastes while content-based ones favour items that found to be similar with the items a user likes. Weak related entities, which are often considered to be useful, are neglected by those similarity-driven recommenders. To take advantage of this neglected information, we introduce a novel dissimilarity-based recommender that bases its estimations on degrees of dissimilarities among items’ attributes.