TF-IDF (short for term frequency-inverse document frequency) is a technique in natural language processing and information retrieval used to evaluate the relevance of a document to a search query by measuring the importance of each word or term in the document.
The concept of TF-IDF was first introduced in the 1970s by researchers Karen Spärck Jones and Stephen Robertson at the University of Cambridge. They proposed using the term frequency and inverse document frequency as a way to determine the relevance of words in a document, and this method has since become a fundamental technique in information retrieval and natural language processing.
The basic idea behind TF-IDF is to assign a weight to each term in a document based on how frequently it appears in the document (term frequency) and how rare it is across all documents in the corpus (inverse document frequency).
The simplified formula for TF-IDF is:
TF-IDF(term, document) = TF(term, document) x IDF(term)
where TF(term, document) is the frequency of the term in the document, and IDF(term) is the inverse document frequency of the term, which is calculated as follows:
IDF(term) = log(N / DF(term))
where N is the total number of documents in the corpus, and DF(term) is the number of documents that contain the term.
In other words, the TF-IDF score for a term in a document is high if the term appears frequently in the document and is rare across all other documents in the corpus.
TF-IDF is important because it was one of the first techniques used in information retrieval, laying the foundation for more advanced modern processing methods.
TF-IDF is still widely used in many digital libraries, databases, and archives for finding relevant documents.
No, TF-IDF is not a direct ranking factor for Google.
While it was a useful metric in the past, there are now many other more advanced information retrieval techniques used by search engines today. Using TF-IDF alone would be too simplistic and easily manipulated.
No, you cannot optimize your pages for TF-IDF. Doing so would mean simply repeating the same keyword across the document, which would be keyword stuffing.
Instead, focus on creating high-quality, informative content that uses relevant keywords naturally and in context.