TF-IDF, which stands for Term Frequency-Inverse Document Frequency, is a numerical statistic used to evaluate the importance of a word in a document relative to a collection of documents or a database. In the context of full-text search, it helps identify which documents are most relevant to a search query. The core idea behind TF-IDF is twofold: the more frequently a term appears in a specific document (Term Frequency, or TF), the more important it is to that document. However, a word’s relevance is diminished if it appears in many documents across the database (Inverse Document Frequency, or IDF), meaning common words like "the" or "and" are less significant.
To calculate TF-IDF for a term in a document, developers first assess the term frequency by counting how many times the term appears in the document and normalizing that by the total number of terms in the document. Next, they calculate its inverse document frequency by taking the logarithm of the total number of documents divided by the number of documents containing the term. The product of these two values gives a TF-IDF score, indicating the term's weight in that document compared to the whole collection.
In practical applications, TF-IDF allows search engines to rank documents based on their relevance to a user’s query. For instance, if a user searches for "machine learning," a document that frequently mentions that phrase while having a low occurrence of common terms will score higher than a document that simplyhas the term in a vague context. This scoring method is fundamental in information retrieval systems, helping to filter out irrelevant results and present the most pertinent information in response to user queries efficiently.