@inproceedings{zhou-etal-2022-aligning,
title = "Aligning Recommendation and Conversation via Dual Imitation",
author = "Zhou, Jinfeng and
Wang, Bo and
Huang, Minlie and
Zhao, Dongming and
Huang, Kun and
He, Ruifang and
Hou, Yuexian",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.36",
doi = "10.18653/v1/2022.emnlp-main.36",
pages = "549--561",
abstract = "Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (\textbf{D}ual \textbf{I}mitation for \textbf{C}onversational \textbf{R}ecommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics.",
}
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<abstract>Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (Dual Imitation for Conversational Recommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics.</abstract>
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%0 Conference Proceedings
%T Aligning Recommendation and Conversation via Dual Imitation
%A Zhou, Jinfeng
%A Wang, Bo
%A Huang, Minlie
%A Zhao, Dongming
%A Huang, Kun
%A He, Ruifang
%A Hou, Yuexian
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhou-etal-2022-aligning
%X Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (Dual Imitation for Conversational Recommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics.
%R 10.18653/v1/2022.emnlp-main.36
%U https://aclanthology.org/2022.emnlp-main.36
%U https://doi.org/10.18653/v1/2022.emnlp-main.36
%P 549-561
Markdown (Informal)
[Aligning Recommendation and Conversation via Dual Imitation](https://aclanthology.org/2022.emnlp-main.36) (Zhou et al., EMNLP 2022)
ACL
- Jinfeng Zhou, Bo Wang, Minlie Huang, Dongming Zhao, Kun Huang, Ruifang He, and Yuexian Hou. 2022. Aligning Recommendation and Conversation via Dual Imitation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 549–561, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.