Category Aware Explainable Conversational Recommendation

Abstract

Most conversational recommendation approaches are either not explainable, or they require external user's knowledge for explaining or their explanations cannot be applied in real time due to computational limitations. In this work, we present a real time category based conversational recommendation approach, which can provide concise explanations without prior user knowledge being required. We first perform an explainable user model in the form of preferences over the items' categories, and then use the category preferences to recommend items. The user model is performed by applying a BERT-based neural architecture on the conversation. Then, we translate the user model into item recommendation scores using a Feed Forward Network. User preferences during the conversation in our approach are represented by category vectors which are directly interpretable. The experimental results on the real conversational recommendation dataset ReDial demonstrate comparable performance to the state-of-the-art, while our approach is explainable. We also show the potential power of our framework by involving an oracle setting of category preference prediction.

arXiv commentWorkshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS) @ECIR, 2021

References in corpus (9)

• MMALFM: Explainable Recommendation by Leveraging Reviews and Images
• Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
• Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation
• Towards Deep Conversational Recommendations
• Pointing the Unknown Words
• Deep Conversational Recommender in Travel
• Towards Knowledge-Based Recommender Dialog System
• The FacT: Taming Latent Factor Models for Explainability with Factorization Trees
• AspeRa: Aspect-based Rating Prediction Model