Modeling Multiple User Interests using Hierarchical Knowledge for Conversational Recommender System
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Abstract (arXiv)
A conversational recommender system (CRS) is a practical application for item recommendation through natural language conversation. Such a system estimates user interests for appropriate personalized recommendations. Users sometimes have various interests in different categories or genres, but existing studies assume a unique user interest that can be covered by closely related items. In this work, we propose to model such multiple user interests in CRS. We investigated its effects in experiments using the ReDial dataset and found that the proposed method can recommend a wider variety of items than that of the baseline CR-Walker.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 1 Mar 2023.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
- 2017Neural Collaborative Filteringcited by 1,111
- 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,077
- 2018Towards Deep Conversational Recommendationscited by 176
- 2020Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusioncited by 122
- 2014Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognitioncited by 111
- 2020CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendationcited by 31
- 2021HieRec: Hierarchical User Interest Modeling for Personalized News Recommendationcited by 31
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