title: Continuous collaborative filtering using Mahout
body: <p>I am in the process of evaluating Mahout as a collaborative-filtering-recommendation engine. So far it looks great.
  We have almost 20M boolean recommendations from 12M different users. 
  According to <a href="https://cwiki.apache.org/MAHOUT/recommender-first-timer-faq.html" rel="nofollow noreferrer">Mahout's wiki</a> and a few threads by <a href="https://stackoverflow.com/users/64174/sean-owen">Sean Owen</a>, one machine should sufficient in this case. Because of that I decided to go with MySql as the data-model and skip the overhead of using Hadoop for now.</p>
  
  <p>One thing eludes me though, what are the best practices for continuously updating the recommendations without reading the whole data from scratch? We have tens-of-thousands of new recommendations every day. While I do not expect it to be processed at real-time, I would like to have it processed every 15 minutes or so.</p>
  
  <p>Please elaborate on the approaches for both a Mysql-based and Hadoop-based deployment.
  Thanks!</p>