TEARS: Textual Representations for Scrutable Recommendations

Emiliano Penaloza, Olivier Gouvert, Haolun Wu +1

Traditional recommender systems rely on high-dimensional (latent) embeddings for modeling user-item interactions, often resulting in opaque representations that lack interpretabili…