• Journal of the Korea Society of Computer and Information
• /
• v.29 no.11
• /
• pp.67-77
• /
• 2024

This paper proposes a method to improve the search functionality for website posts and product reviews by using a ResNet-Transformer model in conjunction with the BM25 ranking algorithm. BM25 is a widely used algorithm in text-based search that ranks documents by evaluating their relevance to user queries. However, it has limitations in capturing local features of words and understanding the context of a sentences. To address these issues, this study applies a classification approach that combines the ResNet model, which excels at extracting local features, with the Transformer model, known for its strong contextual understanding, as weights for BM25. Experimental results demonstrate that the proposed method improves the nDCG metric by 9.38% and the aP@5 metric by 11.82% compared to BM25 alone. This suggests that implementing this method in search engines across various websites can provide more accurate results for post and review searches.