Vector space model: Difference between revisions

Revision as of 04:37, 28 February 2019

Revision as of 10:00, 16 July 2019

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* <math>\mathrm{tf}_{t,d}</math> is term frequency of term ''t'' in document ''d'' (a local parameter)

* <math>\log{\frac{|D|}{|\{d' \in D \, | \, t \in d'\}|}}</math> is inverse document frequency (a global parameter). <math>|D|</math> is the total number of documents in the document set; <math>|\{d' \in D \, | \, t \in d'\}|</math> is the number of documents containing the term ''t''.

Using the cosine the similarity between document ''d_j'' and query ''q'' can be calculated as:

:<math>\mathrm{cos}(d_j,q) = \frac{\mathbf{d_j} \cdot \mathbf{q}}{\left\| \mathbf{d_j} \right\| \left \| \mathbf{q} \right\|} = \frac{\sum _{i=1}^N w_{i,j}w_{i,q}}{\sqrt{\sum _{i=1}^N w_{i,j}^2}\sqrt{\sum _{i=1}^N w_{i,q}^2}}</math>

==Advantages==