IDF of a query?
How do I calculate tf-idf for a query? I understand how to calculate tf-idf for a set of documents with following definitions:
tf = occurances in document/ total words in document
idf = log(#documents / #documents where term occurs
But I don't understand how that correlates to queries.
For example , I read a resource that stated the values of a query " life learning "
life | tf = .5 | idf = 1.405507153 | tf_idf = 0.702753576
learning | tf = .5 | idf = 1.405507153 | tf_idf = 0.702753576
The tf values I understand, each term appears only once out of the two possible terms, thus 1/2, But I have no idea where the idf comes from.
I would think that #documents = 1 and occurrence = 1, log(1) = 0, so idf would be 0, but this doesn't seem to be the case. Is it based on whatever documents you're using? How do you calculate tf-idf for a query?
Only tf(life) depends on the query itself. However, the idf of a query depends on the background documents, so idf(life) = 1+ ln(3/2) ~= 1.405507153. That is why tf-idf is defined as multiplying a local component (term frequency) with a global component (inverse document frequency).
Assume your query is best car insurance , your total vocabulary contains car, best, auto, insurance and you have N=1,000,000 documents. So your query is something like below:
And one of your document could be:
Now calculate cosine similarity between TF-IDF of your Query and Document .
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