Explainable Recommendation: A Survey and New Perspectives

Understand

Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations.

• The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some contexts).
• Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers.
• Explainable recommendation helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommendation systems.