An Empirical Analysis on Multi-turn Conversational Recommender Systems

Author:

Zhang Lu1ORCID,Li Chen2ORCID,Lei Yu2ORCID,Sun Zhu3ORCID,Liu Guanfeng4ORCID

Affiliation:

1. Chengdu University of Information Technology & SUGON Industrial Control and Security Center, Chengdu, China

2. School of Information Science and Engineering, Yanshan University, Qinhuangdao, China

3. Singapore University of Technology and Design, Singapore, Singapore

4. Macquarie University, Sydney, Australia

Publisher

ACM

Reference55 articles.

1. Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Tallrec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. arXiv preprint arXiv:2305.00447 (2023).

2. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-Relational Data. Advances in neural information processing systems , Vol. 26 (2013).

3. CP-Rec: Contextual Prompting for Conversational Recommender Systems

4. Towards Conversational Recommender Systems

5. Yang Deng, Yaliang Li, Bolin Ding, and Wai Lam. 2022. Leveraging Long Short-Term User Preference in Conversational Recommendation via Multi-Agent Reinforcement Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE) (2022).

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