Untangling Relevance Score Boosting and Decay Functions

The blog post delves into the intricacies of relevance score boosting and decay functions within the Qdrant search engine, aiming to demystify their applications for users. It focuses on three types of decay functions—Linear, Gaussian, and Exponential—each of which adjusts the relevance score based on how a numeric property deviates from an ideal value. These functions transform dataset properties, like size or ratings, into scores between 1.0 (most relevant) and 0.0 (not relevant), allowing them to influence the final relevance score meaningfully. Key parameters such as x, target, scale, and midpoint are explained to help users tailor decay functions to match their definitions of relevance, using examples like video lengths and promo code freshness. The article provides practical advice on setting these parameters and discusses the challenges of dynamically normalizing scores without pre-known parameters, emphasizing the importance of understanding the context of input data for effective score boosting.