Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study

In recent years, recommender systems have used advanced machine-learning techniques to improve recommendation precision. However, many of these approaches, like deep-learning, are considered black-box models; in other words, users struggle to comprehend why the system recommends a particular item, affecting the user’s confidence in the provided recommendation. Some recommender systems include methods to explain a recommendation. A common technique is applying explanations-by-examples, but sometimes users do not understand the link between the recommendation and the examples. This paper describes a user study where we have evaluated a novel method to create graph-based explanations. It uses Formal Concept Analysis to extract the most relevant attributes to relate the recommendation with each example. Next, this method shows an interactive graph to users that explains the recommendation based on the links extracted before. Results show a high level of satisfaction regarding the explanations and their visualisation compared with other approaches found in the literature.