Dynamic Thresholding in Document Indexing: An Adaptive Scoring Approach for Retrieval Optimization

In order to enhance search recall and precision, it is essential to have the capacity to dynamically adjust relevance score thresholds in the constantly evolving field of information retrieval. This research introduces a novel dynamic thresholding paradigm that surpasses static approaches and offers an extensible framework for the Okapi-BM25 retrieval method that can be applied to a variety of datasets. We propose a system design that replicates real-world search scenarios by automatically generating sample queries from newly imported files. The extraction of data segments is directed by these queries, and the scores are statistically analyzed to establish dynamic thresholds by removing outliers and calculating mean scores, among other things. This procedure is a component of a modular system that encompasses scoring, query simulation, data ingestion, and a database administration component that modifies thresholds in response to new data. Utilizing publicly accessible retrieval methodologies and employing Python for statistical analysis, this research demonstrates the capacity of dynamic thresholding to substantially enhance information retrieval results. It provides valuable insights into the practicality and scalability of this approach in real-world scenarios.