Design of an Improved Model for Information Retrieval Using BERT and Weighted User Clicks

Abstract

The need for enhancing information retrieval systems has become critical with the exponential growth of web content, which presents significant challenges in terms of data heterogeneity and user query satisfaction. Existing retrieval methods often suffer from limitations such as inadequate handling of unstructured data and suboptimal ranking of search results, leading to lower precision, accuracy, and recall.To address these challenges, we propose a novel framework for information retrieval that leverages web content mining and advanced natural language processing (NLP) techniques. The framework begins by preprocessing the input query to remove stop words using NLP, thereby refining the query for better relevance. We transform the unstructured web content into a structured format by systematically storing web content and user click data samples. This structured data serves as the foundation for our reranking mechanism.Our framework utilizes Bidirectional Encoder Representations from Transformers (BERT) to match web content effectively.