Acknowledgement
이 논문은 2024년도 과학기술정보통신부 재원으로 한국식품연구원의 지원(기본사업 E0220700)을 받아 수행된 연구성과입니다. 본 연구는 2024년 과학기술정보통신부 및 정보통신기획평가원의 SW중심 대학지원사업의 지원을 받아 수행되었음(2022-0-01067).
References
- N. Asadi and J. Lin, "Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures," In Proceedings of the 36th international ACM SIGIR Conference on Research and Development in Information Retrieval, pp.997-1000, 2013.
- R. C. Chen, L. Gallagher, R. Blanco, and J. S. Culpepper, "Efficient cost-aware cascade ranking in multi-stage retrieval. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.445-454, 2017.
- L. Gao, Z. Dai, and J. Callan, "Rethink training of BERT rerankers in multi-stage retrieval pipeline," In Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28-April 1, 2021, Proceedings, Part II 43 (pp.280-286). Springer International Publishing, 2021.
- Y. Nie, S. Wang, and M. Bansal, "Revealing the importance of semantic retrieval for machine reading at scale," arXiv preprint arXiv:1909.08041, 2019.
- A. Aizawa, "An information-theoretic perspective of tf-idf measures," Information Processing & Management, Vol.39, No.1, pp.45-65, 2003.
- Y. Liu et al., "Roberta: A robustly optimized bert pretraining approach," arXiv preprint arXiv:1907.11692, 2019.
- V. Karpukhin et al., "Dense passage retrieval for open-domain question answering," arXiv preprint arXiv:2004.04906, 2020.
- X. Ma, J. Guo, R. Zhang, Y. Fan, Y. Li, and X. Cheng, "B-PROP: bootstrapped pre-training with representative words prediction for ad-hoc retrieval," In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.1513-1522, 2021.
- J. Zhan, J. Mao, Y. Liu, J. Guo, M. Zhang, and S. Ma, "Optimizing dense retrieval model training with hard negatives," In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.1503-1512, 2021.
- J., Zhan, J., Mao, Y., Liu, M., Zhang, and S. Ma, "Repbert: Contextualized text embeddings for first-stage retrieval," arXiv preprint arXiv:2006.15498, 2020.
- P. S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck, "Learning deep structured semantic models for web search using clickthrough data," In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp.2333-2338, 2013.
- H. Shan, Q. Zhang, Z. Liu, G. Zhang, and C. Li, "Beyond two-tower: Attribute guided representation learning for candidate retrieval," In Proceedings of the ACM Web Conference 2023, pp.3173-3181, 2023.
- Q., Zhang et al., "A semantic alignment system for multilingual query-product retrieval," arXiv preprint arXiv: 2208.02958, 2022.
- X. Qin, N. Liang, H. Zhang, W. Zou, and W. Zhang, "Second place solution of Amazon KDD Cup 2022: ESCI Challenge for Improving Product Search," 2022.
- J. Lin, L. Xue, Z. Ying, C. Meng, W. Wang, H. Wang, and X. Wu, "A Winning Solution of KDD CUP 2022 ESCI Challenge for Improving Product Search," 2022.
- J. Park, W. Choi, G. An, and K. Lee, "Deep learning based reranking model for product search," Digital Contents Society, pp.131-132 2023.
- G. An, W. Choi, J. Park, and K. Lee, "JBNU at TREC 2023 Product Search Track," The Thirty-Second Text REtrieval Conference (TREC 2023), 2023.
- V. Ashish, "Attention is all you need," arXiv preprint arXiv: 1706.03762, 2017.
- T. Barrus, "pyspellchecker," 2024, accessed: 20.02.2024. [Internet], https://pypi.org/project/pyspellchecker/
- P. He, X. Liu, J. Gao, and W. Chen, "Deberta: Decoding-enhanced bert with disentangled attention," arXiv preprint arXiv:2006.03654, 2020.