Knowledge-aware Autoencoders for Explainable Recommender Sytems
This work addresses the need for explainable AI in recommender systems to improve user satisfaction and trust, but it is incremental as it builds on existing knowledge graph and autoencoder methods.
The paper tackles the problem of generating human-understandable explanations in recommender systems by evaluating how different types of knowledge graph information (categorical, factual, or mixed) affect explanatory criteria, with results validated through A/B testing using a semantics-aware autoencoder.
Recommender Systems have been widely used to help users in finding what they are looking for thus tackling the information overload problem. After several years of research and industrial findings looking after better algorithms to improve accuracy and diversity metrics, explanation services for recommendation are gaining momentum as a tool to provide a human-understandable feedback to results computed, in most of the cases, by black-box machine learning techniques. As a matter of fact, explanations may guarantee users satisfaction, trust, and loyalty in a system. In this paper, we evaluate how different information encoded in a Knowledge Graph are perceived by users when they are adopted to show them an explanation. More precisely, we compare how the use of categorical information, factual one or a mixture of them both in building explanations, affect explanatory criteria for a recommender system. Experimental results are validated through an A/B testing platform which uses a recommendation engine based on a Semantics-Aware Autoencoder to build users profiles which are in turn exploited to compute recommendation lists and to provide an explanation.