English
TF.IDF (term frequency – inverse document frequency) is a numerical statistic that indicates how important a word or phrase is to a document within a collection of documents.
It is a score that is proportional to the number of times a word appears in the document offset by the frequency of the word in the collection of documents.
It’s calculated by multiplying the “term frequency” of the word appearing in the document and the “inverse document frequency” of the word across all documents.
It’s designed to measure how relevant a term is to a particular document compared to other documents in the set.
In people-oriented searches like job postings, TF.IDF can help identify words related to certain positions or roles, so employers can accurately compare resumes with job qualifications.
(See also BM25)
Term Frequency
Term frequency measures how often a term appears in a document, playing a key role in search relevance, information retrieval, and ranking algorithms.
Search Results
Search results are the pages, documents, or data sets returned in response to a user’s search query, helping them find relevant information.
Long Tail
Long tail refers to less popular, niche search queries or items with significant relevance and impact.
Taxonomy
Taxonomy organizes objects into a tree-like structure, simplifying retrieval and sorting for effortless access.
Search Glossary
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Linguistic Indexing
Linguistic indexing is a classification of sets of words into grammatical classes, such as nouns, adjectives, or verbs.
Thesaurus
Thesaurus cross-references words, links taxonomies, and aids document retrieval through meta-classification.
Metadata
Metadata enhances indexing by supplementing and clarifying document content, improving searchability and retrieval accuracy for better user experience.
