title: Recall, Recall rate@k and precision in top-k recommendation
body: <p>According to authors in <a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4222611&amp;tag=1" rel="noreferrer">1</a>, <a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6671293" rel="noreferrer">2</a>, and <a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6100061" rel="noreferrer">3</a>, <strong>Recall</strong> is the percentage of relevant items selected out of all the relevant items in the repository, while <strong>Precision</strong> is the percentage of relevant items out of those items selected by the query.</p>
  
  <p>Therefore, assuming user <em>U</em> gets a top-<em>k</em> recommended list of items, they would be something like:</p>
  
  <p><strong>Recall</strong>= (Relevant_Items_Recommended in top-<em>k</em>) <strong>/</strong> (Relevant_Items)</p>
  
  <p><strong>Precision</strong>= (Relevant_Items_Recommended in top-<em>k</em>) <strong>/</strong> (<em>k</em>_Items_Recommended)</p>
  
  <p>Until that part everything is clear but I do not understand the difference between them and <strong>Recall rate@k</strong>. How would be the formula to compute <strong>recall rate@k</strong>?</p>