Conversational Recommendation System Based on Utterance Act and Emotion

An Jiahao, Dang Huibo
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:155-164, 2024.

Abstract

Conversational recommendation system (CRS) aims to acquire user preferences of conversation and then make recommendations to users. Existing CRS enhance the characterization of items by introducing external information to improve the recommendation effect, but they ignore the most essential utterance semantic attributes in the dialogue, and do not fully consider the different emotion or act feedback of users. Based on this, this paper fully explores the utterance semantics, makes the construction of user characteristics fuller by extracting the act and emotion of the utterance, and predicts the act of the conversation process, and then enhances the response through the emotional vocabulary, to improve the interaction experience between the user and the system. A large number of experiments on public datasets show that the model proposed in this paper outperforms the most advanced methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-jiahao24a, title = {Conversational Recommendation System Based on Utterance Act and Emotion}, author = {Jiahao, An and Huibo, Dang}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {155--164}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/jiahao24a/jiahao24a.pdf}, url = {https://proceedings.mlr.press/v245/jiahao24a.html}, abstract = {Conversational recommendation system (CRS) aims to acquire user preferences of conversation and then make recommendations to users. Existing CRS enhance the characterization of items by introducing external information to improve the recommendation effect, but they ignore the most essential utterance semantic attributes in the dialogue, and do not fully consider the different emotion or act feedback of users. Based on this, this paper fully explores the utterance semantics, makes the construction of user characteristics fuller by extracting the act and emotion of the utterance, and predicts the act of the conversation process, and then enhances the response through the emotional vocabulary, to improve the interaction experience between the user and the system. A large number of experiments on public datasets show that the model proposed in this paper outperforms the most advanced methods. } }
Endnote
%0 Conference Paper %T Conversational Recommendation System Based on Utterance Act and Emotion %A An Jiahao %A Dang Huibo %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-jiahao24a %I PMLR %P 155--164 %U https://proceedings.mlr.press/v245/jiahao24a.html %V 245 %X Conversational recommendation system (CRS) aims to acquire user preferences of conversation and then make recommendations to users. Existing CRS enhance the characterization of items by introducing external information to improve the recommendation effect, but they ignore the most essential utterance semantic attributes in the dialogue, and do not fully consider the different emotion or act feedback of users. Based on this, this paper fully explores the utterance semantics, makes the construction of user characteristics fuller by extracting the act and emotion of the utterance, and predicts the act of the conversation process, and then enhances the response through the emotional vocabulary, to improve the interaction experience between the user and the system. A large number of experiments on public datasets show that the model proposed in this paper outperforms the most advanced methods.
APA
Jiahao, A. & Huibo, D.. (2024). Conversational Recommendation System Based on Utterance Act and Emotion. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:155-164 Available from https://proceedings.mlr.press/v245/jiahao24a.html.

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