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English Information

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Authors
# Name
1 Felipe Soares Fagundes Paula(fsfpaula@inf.ufrgs.br)
2 Daniel Kuhn(daniel.kuhn@vtex.com)
3 Viviane Pereira Moreira (viviane@inf.ufrgs.br)

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Reference
# Reference
1 Bencke, L., Paula, F. S., dos Santos, B. G., and Moreira, V. P. (2024). Can we trust llms as relevance judges? In Simpósio Brasileiro de Banco de Dados (SBBD), pages 600–612. SBC.
2 Chen, Y., Liu, S., Liu, Z., Sun, W., Baltrunas, L., and Schroeder, B. (2022). Wands: Dataset for product search relevance assessment. In European Conference on IR Research, page 128–141.
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6 Fang, C., Li, X., Fan, Z., Xu, J., Nag, K., Korpeoglu, E., Kumar, S., and Achan, K. (2024). Llm-ensemble: Optimal large language model ensemble method for e-commerce product attribute value extraction. In Conference on Research and Development in Information Retrieval, SIGIR ’24, page 2910–2914.
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10 Luo, C., Goutam, R., Zhang, H., Zhang, C., Song, Y., and Yin, B. (2023). Implicit query parsing at amazon product search. In Conference on Research and Development in Information Retrieval, SIGIR ’23, page 3380–3384.
11 Luo, C., Tang, X., Lu, H., Xie, Y., Liu, H., Dai, Z., Cui, L., Joshi, A., Nag, S., Li, Y., Li, Z., Goutam, R., Tang, J., Zhang, H., and He, Q. (2024). Exploring query understanding for amazon product search.
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13 Reddy, C. K., Màrquez, L., Valero, F., Rao, N., Zaragoza, H., Bandyopadhyay, S., Biswas, A., Xing, A., and Subbian, K. (2022). Shopping queries dataset: A large-scale esci benchmark for improving product search.
14 Sachdev, J., D Rosario, S., Phatak, A., Wen, H., Kirti, S., and Tripathy, C. (2025). Automated query-product relevance labeling using large language models for e-commerce search. In International Conference on Natural Language Processing and Information Retrieval, NLPIR ’24, page 32–40.
15 Schamber, L., Eisenberg, M. B., and Nilan, M. S. (1990). A re-examination of relevance: toward a dynamic, situational definition. Information processing & management, 26(6):755–776.
16 Sondhi, P., Sharma, M., Kolari, P., and Zhai, C. (2018). A taxonomy of queries for e-commerce search. In Conference on Research & Development in Information Retrieval, SIGIR ’18, page 1245–1248.
17 Soviero, B., Kuhn, D., Salle, A., and Moreira, V. P. (2024). Chatgpt goes shopping: Llms can predict relevance in ecommerce search. In Advances in Information Retrieval, pages 3–11.
18 Su, N., He, J., Liu, Y., Zhang, M., and Ma, S. (2018). User intent, behaviour, and perceived satisfaction in product search. In ACM International Conference on Web Search and Data Mining, WSDM ’18, page 547–555.
19 Thomas, P., Spielman, S., Craswell, N., and Mitra, B. (2024). Large language models can accurately predict searcher preferences. In Conference on Research and Development in Information Retrieval, SIGIR ’24, page 1930–1940.
20 Tsagkias, M., King, T. H., Kallumadi, S., Murdock, V., and De Rijke, M. (2021). Challenges and research opportunities in ecommerce search and recommendations. In ACM Sigir Forum, volume 54, pages 1–23.
21 Voorhees, E. (2007). Overview of trec 2006.
22 Wu, C.-Y., Ahmed, A., Kumar, G. R., and Datta, R. (2017). Predicting latent structured intents from shopping queries. In International Conference on World Wide Web, WWW’17, page 1133–1141.
23 Yang, H., Gupta, P., Fernández Galán, R., Bu, D., and Jia, D. (2021). Seasonal relevance in e-commerce search. In ACM International Conference on Information & Knowledge Management, CIKM ’21, page 4293–4301.
24 Zhang, D., Li, Z., Cao, T., Luo, C., Wu, T., Lu, H., Song, Y., Yin, B., Zhao, T., and Yang, Q. (2021). Queaco: Borrowing treasures from weakly-labeled behavior data for query attribute value extraction. In ACM International Conference on Information & Knowledge Management, CIKM ’21, page 4362–4372.
25 Zhu, Y., Vedula, N., and Malmasi, S. (2025). Hint-augmented re-ranking: Efficient product search using LLM-based query decomposition. In Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Associationfor Computational Linguistics, pages 200–216.