Report
Category Aware Explainable Conversational Recommendation
| العنوان: | Category Aware Explainable Conversational Recommendation |
|---|---|
| المؤلفون: | Kondylidis, Nikolaos, Zou, Jie, Kanoulas, Evangelos |
| المصدر: | Workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS) @ECIR, 2021 |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Information Retrieval, Computer Science - Machine Learning |
| الوصف: | 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. Comment: Workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS) @ECIR, 2021 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2103.08733 |
| رقم الانضمام: | edsarx.2103.08733 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |