<p>In which way are the results different?</p>

<p>What do you mean by "confidential parameters"?</p>

<p>3 interesting papers that also deal with the interpretation of 0-1 data in the context of recommender systems (I am a co-author on one of those):</p>

<ol>
<li>Hu et al.: <a href="http://www.hpl.hp.com/techreports/2008/HPL-2008-48R1.pdf" rel="nofollow">Collaborative filtering for implicit feedback datasets</a></li>
<li>Rendle et al.: <a href="http://arxiv.org/pdf/1205.2618" rel="nofollow">BPR: Bayesian personalized ranking from implicit feedback</a></li>
<li>Pan et al.: <a href="http://www.hpl.hp.com/techreports/2008/HPL-2008-48R1.pdf" rel="nofollow">One-class collaborative filtering</a></li>
</ol>
matrix factorization for 0-1 data
<p>I use 0-1 data to train matrix factorization (MF) model and use recall to eval the performance. For zero data, we can interpret as two ways. First, user does not like it, roughly. Second, user does not know about it or does not like it. In the first condition, I sampling random negative samples and use gradient descend. In the latter case, I use confidential parameters and iteratively update analytic expression. I found they give totally different results and I am puzzled by this. Could someone help me?</p>