Enhancing Explainability and Scrutability of Recommender Systems

Abstract

Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations as end users and the algorithm's behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in filtering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. To this end, we put forward proposals for explaining recommendations to the end users. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Such explanations usually contain valuable clues as to how a system perceives user preferences and more importantly how its behavior can be modified. Therefore, as a natural next step, we develop a framework for leveraging user feedback on explanations to improve their future recommendations. We evaluate all the proposed models and methods with real user studies and demonstrate their benefits at achieving explainability and scrutability in recommender systems.