@inproceedings{
zheng2024diversity,
title={Diversity Matters: User-Centric Multi-Interest Learning for Conversational Movie Recommendation},
author={Yongsen Zheng and Guohua Wang and Yang Liu and Liang Lin},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=JDiR6JYHZw}
}
Diversity plays a crucial role in Recommender Systems (RSs) as it ensures a wide range of recommended items, providing users with access to new and varied options. Without diversity, users often encounter repetitive content, limiting their exposure to novel choices. While significant efforts have been dedicated to enhancing recommendation diversification in static offline scenarios, relatively less attention has been given to online Conversational Recommender Systems (CRSs). However, the lack of recommendation diversity in CRSs will increasingly exacerbate over time due to the dynamic user-system feedback loop, resulting in challenges such as the Matthew effect, filter bubbles, and echo chambers. To address these issues, we propose an innovative end-to-end CRS paradigm called User-Centric Multi-Interest Learning for Conversational Movie Recommendation (CoMoRec), which aims to learn user interests from multiple perspectives to enhance result diversity as users engage in natural language conversations for movie recommendations. Firstly, CoMoRec automatically models various facets of user interests, including context-based, graph-based, and review-based interests, to explore a wide range of user intentions and preferences. Then, it leverages these multi-aspect user interests to accurately predict personalized and diverse movie recommendations and generate fluent and informative responses during conversations. Through extensive experiments conducted on two publicly available CRS-based movie datasets, our proposed CoMoRec achieves a new state-of-the-art performance and outperforms all the compared baselines in terms of improving recommendation diversity in the CRS.
Diversity Matters: User-Centric Multi-Interest Learning for Conversational Movie Recommendation
Yongsen Zheng Guohua Wang Yang Liu Liang Lin