Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability

Understand

Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items.

• Recently, explainable recommendation has attracted much attention from research community.
• However, trade-off exists between explainability and performance of the recommendation where metadata is often needed to alleviate the dilemma.
• We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactory accuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss.