Web Page Classification using LLMs for Crawling Support
This work addresses the challenge of efficient web crawling for systems that collect web pages, but it is incremental as it applies an existing LLM method to a specific domain task.
The study tackled the problem of efficiently collecting new web pages by classifying them into 'Index Pages' and 'Content Pages' using a large language model (LLM), and using this classification to select starting points for crawling. Experimental results showed that the LLM-based method outperformed baseline methods in classification performance and coverage of new pages.
A web crawler is a system designed to collect web pages, and efficient crawling of new pages requires appropriate algorithms. While website features such as XML sitemaps and the frequency of past page updates provide important clues for accessing new pages, their universal application across diverse conditions is challenging. In this study, we propose a method to efficiently collect new pages by classifying web pages into two types, "Index Pages" and "Content Pages," using a large language model (LLM), and leveraging the classification results to select index pages as starting points for accessing new pages. We construct a dataset with automatically annotated web page types and evaluate our approach from two perspectives: the page type classification performance and coverage of new pages. Experimental results demonstrate that the LLM-based method outperformed baseline methods in both evaluation metrics.