<p><a href="https://arxiv.org/pdf/1511.06939.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1511.06939.pdf</a></p>

<p>In this paper, recall is calculated as "proportion of cases having the desired item amongst the top-k items in all test cases."</p>
How to calculate Recall for Recurrent Neural Network (RNN) based Recommendation System
<p>As I know, In Top-N Recommendation System, the formular of Recall is as below :</p>

<pre><code>recall = |{A} and {B}| / |{A}|
</code></pre>

<p><strong>where {A} are the things that user actually bought, {B} are the Top-N things that system recommended.</strong></p>

<p>But in RNN based recommendation system, it is a  little different from the traditional recommendation system such as kNN based recommendation system (user based or item based system).</p>

<p><strong>The target of RNN based recommendation system is to predict the thing that user would probably buy in next time "t+1". In each step, system will give a Top-N recommendation.</strong> Reference paper:<a href="https://arxiv.org/abs/1608.07400" rel="nofollow noreferrer">enter link description here</a></p>

<p>So how to caclulate Recall for Recurrent Neural Network (RNN) based Recommendation System?</p>