title: Time Aware Recommender System would work for my data set?
body: <p>I have implicit feed back data. </p>
  
  <pre><code>Customer Data: &lt;CustomerID&gt; &lt;Product Bought&gt; &lt;Date of Purchase&gt;
                  C1             P1                01-11-2008
                  C1             P2                01-01-2009
                  C1             P3                01-01-2020
                  C2             P1                01-01-2021
  </code></pre>
  
  <p>I am building a recommender system. I have used co-occurrence matrix to build the recommender system. I have used Graph lab to do it. I also used Jaccard similarity measure for it. </p>
  
  <p>Now my objective is to recommend products which customer likely to buy in next 6 months. For C2, I should recommend product P2 and not P3. How to handle this problem? I did learn about CARS (context-aware recommender systems) esp. <a href="https://github.com/ibayer/fastFM" rel="nofollow">Fast FM</a>. It looks like it did not serve me well. Please help me how to handle problem. Item-item similarity is not doing well for me.</p>