@inproceedings{zhang-etal-2022-toward,
title = "Toward Knowledge-Enriched Conversational Recommendation Systems",
author = "Zhang, Tong and
Liu, Yong and
Li, Boyang and
Zhong, Peixiang and
Zhang, Chen and
Wang, Hao and
Miao, Chunyan",
editor = "Liu, Bing and
Papangelis, Alexandros and
Ultes, Stefan and
Rastogi, Abhinav and
Chen, Yun-Nung and
Spithourakis, Georgios and
Nouri, Elnaz and
Shi, Weiyan",
booktitle = "Proceedings of the 4th Workshop on NLP for Conversational AI",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nlp4convai-1.17",
doi = "10.18653/v1/2022.nlp4convai-1.17",
pages = "212--217",
abstract = "Conversational Recommendation Systems recommend items through language based interactions with users. In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie. We also propose an alignment loss to further integrate KG entities into the response generation network. Experiments on the large-scale REDIAL dataset demonstrate that the proposed system consistently outperforms state-of-the-art baselines.",
}
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<abstract>Conversational Recommendation Systems recommend items through language based interactions with users. In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie. We also propose an alignment loss to further integrate KG entities into the response generation network. Experiments on the large-scale REDIAL dataset demonstrate that the proposed system consistently outperforms state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T Toward Knowledge-Enriched Conversational Recommendation Systems
%A Zhang, Tong
%A Liu, Yong
%A Li, Boyang
%A Zhong, Peixiang
%A Zhang, Chen
%A Wang, Hao
%A Miao, Chunyan
%Y Liu, Bing
%Y Papangelis, Alexandros
%Y Ultes, Stefan
%Y Rastogi, Abhinav
%Y Chen, Yun-Nung
%Y Spithourakis, Georgios
%Y Nouri, Elnaz
%Y Shi, Weiyan
%S Proceedings of the 4th Workshop on NLP for Conversational AI
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhang-etal-2022-toward
%X Conversational Recommendation Systems recommend items through language based interactions with users. In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie. We also propose an alignment loss to further integrate KG entities into the response generation network. Experiments on the large-scale REDIAL dataset demonstrate that the proposed system consistently outperforms state-of-the-art baselines.
%R 10.18653/v1/2022.nlp4convai-1.17
%U https://aclanthology.org/2022.nlp4convai-1.17
%U https://doi.org/10.18653/v1/2022.nlp4convai-1.17
%P 212-217
Markdown (Informal)
[Toward Knowledge-Enriched Conversational Recommendation Systems](https://aclanthology.org/2022.nlp4convai-1.17) (Zhang et al., NLP4ConvAI 2022)
ACL