What are the main challenges and trade-offs of designing explainable recommender systems?
Recommender systems are software applications that suggest products, services, or content to users based on their preferences, behavior, or feedback. They are widely used in e-commerce, entertainment, social media, and other domains to enhance user experience and satisfaction. However, recommender systems often face the challenge of providing explanations for their recommendations, which can increase user trust, transparency, and engagement. In this article, you will learn about the main challenges and trade-offs of designing explainable recommender systems, and some possible solutions and best practices.