Untangling Relevance Score Boosting and Decay Functions - Qdrant

This post details the implementation and usage of Linear, Exponential, and Gaussian decay functions within Qdrant's score boosting functionality. It explains the purpose of these functions in transforming numeric properties into relevance scores (1.0 to 0.0) and influencing final relevance. The post clarifies the parameters: 'x' (input variable), 'target' (ideal value for 1.0 relevance), 'scale' (controls decay rate with 'x'), and 'midpoint' (relevance value at 'target ± scale'). It provides examples for use cases like video length, promo code freshness, and price sensitivity, and explicitly states that 'midpoint' and 'scale' are static and cannot be dynamically set during query time, warning against dynamic normalization of scores based on query subsets.