Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions and Prospects-Reference-Cited by-同舟云学术

Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions and Prospects

Published:2023-07-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Qu Xinghua¹^ORCID,Liu Hongyang²^ORCID,Sun Zhu³^ORCID,Yin Xiang²^ORCID,Ong Yew Soon⁴^ORCID,Lu Lu⁵^ORCID,Ma Zejun⁶^ORCID

Affiliation:

1. Bytedance AI Lab, Singapore, China

2. Bytedance AI Lab, Shanghai, China

3. Institute of High Performance Computing, Centre for Frontier AI Research, A*STAR, Singapore, Singapore

4. A*STAR Centre for Frontier AI Research & Nanyang Technological University, Singapore, Singapore

5. Bytedance AI Lab, Hangzhou, China

6. Bytedance AI Lab, Beijing, China

Funder

A*STAR Center for Frontier Artificial Intelligence Research

Data Science and Artificial Intelligence Research Centre, School of Computer Science and Engineering at the Nanyang Technological University, Singapore

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539618.3591876

Reference52 articles.

1. Towards Knowledge-Based Recommender Dialog System

2. Wide & Deep Learning for Recommender Systems

3. Alexis Conneau , Alexei Baevski , Ronan Collobert , Abdelrahman Mohamed , and Michael Auli . 2020. Unsupervised cross-lingual representation learning for speech recognition. arXiv preprint arXiv:2006.13979 ( 2020 ). Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, and Michael Auli. 2020. Unsupervised cross-lingual representation learning for speech recognition. arXiv preprint arXiv:2006.13979 (2020).

4. Deep Neural Networks for YouTube Recommendations

5. Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning

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