A graph-based approach for minimising the knowledge requirement of explainable recommender systems

Abstract

Traditionally, recommender systems use collaborative filtering or content-based approaches based on ratings and item descriptions. However, this information is unavailable in many domains and applications, and recommender systems can only tackle the problem using information about interactions or implicit knowledge. Within this scenario, this work proposes a novel approach based on link prediction techniques over graph structures that exclusively considers interactions between users and items to provide recommendations. We present and evaluate two alternative recommendation methods: one item-based and one user-based that apply the edge weight, common neighbours, Jaccard neighbours, Adar/Adamic, and Preferential Attachment link prediction techniques. This approach has two significant advantages, which are the novelty of our proposal. First, it is suitable for minimal knowledge scenarios where explicit data such as ratings or preferences are not available. However, as our evaluation demonstrates, this approach outperforms state-of-the-art techniques using a similar level of interaction knowledge. Second, our approach has another relevant feature regarding one of the most significant concerns in current artificial intelligence research: the recommendation methods presented in this paper are easily interpretable for the users, improving their trust in the recommendations.