Improving Faithfulness and Factuality with Contrastive Learning in Explainable Recommendation
Author:
Affiliation:
1. The University of Adelaide, Adelaide, Australia
2. The University of Adelaide - North Terrace Campus, Adelaide, Australia
3. Computing, Macquarie University, Sydney, Australia
4. Department of Computing, Macquarie University, Sydney, Australia
Abstract
Publisher
Association for Computing Machinery (ACM)
Link
https://dl.acm.org/doi/pdf/10.1145/3653984
Reference49 articles.
1. Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. Knowl. Data Eng.(2005).
2. Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. In Proceedings of the Eleventh International Conference on Learning Representations (ICLR 2023).
3. Shuyang Cao and Lu Wang. 2021. CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
4. Shuo Chang, F. Maxwell Harper, and Loren Gilbert Terveen. 2016. Crowd-Based Personalized Natural Language Explanations for Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys 2016).
5. Hongshen Chen Xiaorui Liu Dawei Yin and Jiliang Tang. 2017. A Survey on Dialogue Systems: Recent Advances and New Frontiers. SIGKDD Explor. Newsl.(2017).
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