How does TF-IDF based ranking function in information retrieval, and what is its significance?

TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure used to evaluate the importance of a word in a document relative to a collection of documents.
The TF-IDF score of a term increases with its frequency in the document (TF) but is offset by the frequency of the term in the corpus (IDF).
In information retrieval, TF-IDF is used to rank documents based on their relevance to a given query. Documents with higher TF-IDF scores for the query terms are considered more relevant.
Its significance lies in its ability to effectively identify and retrieve documents that are most relevant to a user’s query while filtering out commonly occurring terms that are less informative.