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Found 3 papers, 1 papers with code
RecXplainer: Amortized Attribute-based Personalized Explanations for Recommender Systems
We evaluate RecXplainer on five real-world and large-scale recommendation datasets using five different kinds of recommender systems to demonstrate the efficacy of RecXplainer in capturing users' preferences over item attributes and using them to explain recommendations.
Contrastive Learning for Interactive Recommendation in Fashion
Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms.
OutfitTransformer: Learning Outfit Representations for Fashion Recommendation
For compatibility prediction, we design an outfit token to capture a global outfit representation and train the framework using a classification loss.
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