title: Evaluation Metrics for Recommendation Systems
body: <p>I am building a collaborative filtering recommendation engine and i am trying to measure how accurate is my model and the quality of the recommendations. I test my algorithm with the following steps. </p>
  
  <p>1) Train the model with 3 months data ( t )</p>
  
  <p>2) I recommend items for the next day (t1 =  t+1day )</p>
  
  <p>3) Calculate the Accuracy, Precision and Recall from validation set.</p>
  
  <p>As validation i use a 30 days time-space ( t1 + 30days) to check if the user interact with the product</p>
  
  <p>This is the way that  i measure my model now:</p>
  
  <p><strong>Accuracy:</strong> How many times a user buy 1 item from my top 5 recommendation</p>
  
  <p>For Precision and Recall i measure the 2 metrics for every user and then i found the mean Precision and Recall of all my users:</p>
  
  <p><strong>Precision</strong> at Top 5 recommendations: Correct recommendations / 5 </p>
  
  <p><strong>Recall</strong> at Top 5 recommendations: Correct recommendations / Known products that 
  the user buy in 30 days validation</p>
  
  <p>Is the way that i measure the Recall correct?</p>
  
  <p>What the Recall represent in recommendation engines?</p>
  
  <p>Is there any other metrics that i can use?</p>