Search Re-ranking Through Weighted Deep Learning Model 


Vol. 13,  No. 5, pp. 221-226, May  2024
https://doi.org/10.3745/TKIPS.2024.13.5.221


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  Abstract

In information retrieval, queries come in various types, ranging from abstract queries to those containing specific keywords, making it a challenging task to accurately produce results according to user demands. Additionally, search systems must handle queries encompassing various elements such as typos, multilingualism, and codes. Reranking is performed through training suitable documents for queries using DeBERTa, a deep learning model that has shown high performance in recent research. To evaluate the effectiveness of the proposed method, experiments were conducted using the test collection of the Product Search Track at the TREC 2023 international information retrieval evaluation competition. In the comparison of NDCG performance measurements regarding the experimental results, the proposed method showed a 10.48% improvement over BM25, a basic information retrieval model, in terms of search through query error handling, provisional relevance feedback-based product title-based query expansion, and reranking according to query types, achieving a score of 0.7810.

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[IEEE Style]

G. An, W. Choi, J. Park, J. Park, K. Lee, "Search Re-ranking Through Weighted Deep Learning Model," The Transactions of the Korea Information Processing Society, vol. 13, no. 5, pp. 221-226, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.5.221.

[ACM Style]

Gi-Taek An, Woo-Seok Choi, Jun-Yong Park, Jung-Min Park, and Kyung-Soon Lee. 2024. Search Re-ranking Through Weighted Deep Learning Model. The Transactions of the Korea Information Processing Society, 13, 5, (2024), 221-226. DOI: https://doi.org/10.3745/TKIPS.2024.13.5.221.

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