<P> In information retrieval , tf -- idf or TFIDF , short for term frequency -- inverse document frequency , is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus . It is often used as a weighting factor in searches of information retrieval , text mining , and user modeling . The tf - idf value increases proportionally to the number of times a word appears in the document , but is often offset by the frequency of the word in the corpus , which helps to adjust for the fact that some words appear more frequently in general . Nowadays , tf - idf is one of the most popular term - weighting schemes . For instance , 83 % of text - based recommender systems in the domain of digital libraries use tf - idf . </P>
<P> In information retrieval , tf -- idf or TFIDF , short for term frequency -- inverse document frequency , is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus . It is often used as a weighting factor in searches of information retrieval , text mining , and user modeling . The tf - idf value increases proportionally to the number of times a word appears in the document , but is often offset by the frequency of the word in the corpus , which helps to adjust for the fact that some words appear more frequently in general . Nowadays , tf - idf is one of the most popular term - weighting schemes . For instance , 83 % of text - based recommender systems in the domain of digital libraries use tf - idf . </P>
the tf-idf score of a term increases with
the tf-idf score of a term increases with
the number of times a word appears in the document
the number of times a word appears in the document