TF-IDF is a classical information retrieval term weigthing model, which estimates the importance of a term in a given document by multiplying the raw term frequency (TF) of the term in a document by the term's inverse document frequency (IDF) weight:
idf_k = log (NDoc / D_k)
w_kd = f_kd.idf_k
where f_kd is the frequency with which keyword k occurs in document d, NDoc is the total number of documents containing keyword k.
There are many variants of TF-IDF depending on whether TF is normalised and/or how IDF is estimated.
For more information about classical term weighting models could be found in the following paper:
G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24 (5):513--523, 1988.
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