Proceedings - Product Search 2024

JBNU at TREC 2024 Product Search Track

Gi-taek An, Seong-Hyuk Yim, Jun-Yong Park, Woo-Seok Choi, Kyung-Soon Lee

• Participant: jbnu
• Paper: https://trec.nist.gov/pubs/trec33/papers/jbnu.product.pdf
• Runs: jbnu08 | jbnu04 | jbnu09 | jbnu01 | jbnu07 | jbnu10 | jbnu03 | jbnu02 | jbnu11 | jbnu12 | jbnu05 | jbnu06

Abstract

This paper describes the participation of the jbnu team in the TREC 2024 Product Search Track. This study addresses two key challenges in product search related to sparse and dense retrieval models. For sparse retrieval models, we propose modifying the activation function to GELU to filter out products that, despite being retrieved due to token expansion, are irrelevant for recommendation based on the scoring mechanism. For dense retrieval models, product search document indexing data was generated using the generative model T5 to address input token limitations. Experimental results demonstrate that both proposed methods yield performance improvements over baseline models.

Bibtex

@inproceedings{jbnu-trec2024-papers-proc-1,
    title = {JBNU at TREC 2024 Product Search Track},
    author = {Gi-taek An and Seong-Hyuk Yim and Jun-Yong Park and Woo-Seok Choi and Kyung-Soon Lee},
    booktitle = {Proceedings of the 33th Text {REtrieval} Conference (TREC 2024)},
    year = {2024},
    address = {Gaithersburg, Maryland},
    series = {NIST SP 1329}
}