An element-wise contribution-based vector similarity measure for artificial intelligence applications: a brief exploration

Abstract

In Artificial Intelligence (AI), the ability to accurately assess the similarity between data points is fundamental to a myriad of applications, from recommendation systems and semantic analysis to case-based reasoning. Traditional similarity measures, however, often fall short in capturing the relationships inherent in complex data, particularly when the relevance of individual features varies or opposes. This article briefly introduces a novel element-wise contribution-based vector similarity measure that dynamically weighs the importance and directional relevance of features, offering a more refined approach to similarity assessment in AI. By normalizing vectors within specific ranges and employing a modulating vector to adjust feature contributions, our measure facilitates a more contextually aware and adaptable comparison process. The proposed measure is poised to have wide-ranging implications across diverse AI domains, suggesting its potential to enrich personalized, intelligent systems. This work contributes in similarity measurement, proposing a pathway for future research into AI methodologies that necessitate a personalised interpretation of data.