Dissimilarity Features in Recommender Systems

In the context of recommenders, providing suitable suggestions requires an effective content analysis where information for items, in the form of features, can play a significant role. Many recommenders suffer from the absence of indicative features capable of capturing precisely the users' preferences which constitutes a vital requirement for a successful recommendation technique. Aiming to overcome such limitations, we introduce a framework through which we extract dissimilarity features based on differences in preferences of items' attributes among users. We enrich the representations of items with the extracted features for the purpose of increasing the ability of a recommender to highlight the preferred items. In this direction, we incorporate the dissimilarity features into different types of classifiers/recommenders (C4.5 and lib-SVM) and evaluate their importance in terms of precision and relevance. Experimentation on real data (Yahoo! Music Social Network) indicates that the inclusion of the proposed features improves the classifiers' performance, and subsequently the provided recommendations.