ClayRS: An end-to-end framework for reproducible knowledge-aware recommender systems

Abstract

Knowledge-aware recommender systems represent one of the most innovative research directions in the area of recommender systems, aiming at giving meaning to information expressed in natural language and obtaining a deeper comprehension of the information conveyed by textual content.Though rich and constantly evolving, the literature on knowledge-aware recommender systems is particularly scattered when considering software libraries. This makes it difficult to easily exploit advanced content representation and implement replicable experimental protocols. Accordingly, this work aims to fill in these gaps by introducing ClayRS, an end-to-end framework for replicable knowledge-aware recommender systems. ClayRS provides researchers and practitioners with the most recent state-of-the-art methodologies to build knowledge-aware content representations and also includes methods to exploit these representations in content-based recommendation algorithms. Finally, the structure of the framework also allows for building replicable pipelines to push forward the current research in the area and to develop accountable recommender systems.