title: Tag/Keyword based recommendation
body: <p>I am wondering what algorithm would be clever to use for a tag driven e-commerce enviroment:</p>
  
  <ul>
  <li><p>Each item has several tags. IE:</p>
  
  <p><strong>Item name:</strong> "Metallica - Black Album CD", <strong>Tags:</strong> "metallica", "black-album", "rock", "music"</p></li>
  <li><p>Each user has several tags and friends(other users) bound to
  them. IE:</p>
  
  <p><strong>Username:</strong> "testguy", <strong>Interests:</strong> "python", "rock", "metal", "computer-science"
  <strong>Friends:</strong> "testguy2", "testguy3"</p></li>
  </ul>
  
  <p>I need to generate recommendations to such users by checking their interest tags and generating recommendations in a sophisticated way. </p>
  
  <p>Ideas:</p>
  
  <ul>
  <li>A Hybrid recommendation algorithm can be used as each user has friends.(mixture of collaborative + context based recommendations).</li>
  <li><p>Maybe using user tags, similar users (peers) can be found to generate recommendations.</p></li>
  <li><p>Maybe directly matching tags between users and items via tags.</p></li>
  </ul>
  
  <p>Any suggestion is welcome. Any python based library is also welcome as I will be doing this experimental engine on python language.</p>