Relevancescore

Relevancescore is a numeric value used in information retrieval to indicate how well a document matches a user query. It serves as the basis for ranking results, with higher scores suggesting greater relevance. Relevancescores are produced by ranking algorithms that combine signals about the query and the document, and they are typically interpreted within the context of a specific collection and user behavior.

Common methods for computing a relevancescore include traditional lexical approaches and probabilistic or neural techniques. Lexical methods often rely on a vector space model with TF-IDF weighting and a similarity measure such as cosine similarity to compare the query and document representations. Probabilistic approaches, such as BM25, estimate the likelihood that a document will satisfy the query and are widely used in production search systems. Language-model based scores assess how probable the query would be generated from the document, and modern systems increasingly employ neural ranking models that score query-document pairs using learned representations. These neural models may be used as re-rankers or end-to-end rankers, sometimes combining with traditional signals.

Relevancescores are used to order results in search engines, question-answering systems, and other retrieval applications. They are not absolute measures of document quality but reflect the effectiveness of a given ranking function for a particular task and data. Evaluation typically involves comparing rankings against graded relevance judgments and user interaction signals, guiding tuning through learning-to-rank techniques and calibration to improve user satisfaction. Limitations include dependence on data, potential biases, and the difficulty of capturing intent in ambiguous queries.