Explainable Restricted Boltzmann Machines for Collaborative Filtering

Understand

Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations.

• Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations.
• This gap between accuracy and transparency or explainability has generated an interest in automated explanation generation methods.
• Restricted Boltzmann Machines (RBM) are accurate models for CF that also lack interpretability.