title: How to train a classifier with only positive and neutral data?
body: <p>My question : How to train a classifier with only positive and neutral data?</p>
  
  <p>I am building a personalized article recommendation system for education purposes. The data I use is from Instapaper.</p>
  
  <p><strong>Datasets</strong></p>
  
  <p>I only have positive data:
  - Articles that I have read and "liked", regardless of read/unread status</p>
  
  <p>And neutral data (because I have expressed interest in it, but I may not like it later anyway):
  - Articles that are unread
  - Articles that I have read and marked as read but I did not "like" it</p>
  
  <p>The data I do not have is negative data:
  - Articles that I did not send to Instapaper to read it later (I am not interested, although I have browsed that page/article)
  - Articles that I might not even have clicked into, but I might have or might not have archive it.</p>
  
  <p><strong>My problem</strong></p>
  
  <p>In such a problem, negative data is basically missing. I have thought of the following solution(s) but did not resolve to them yet:</p>
  
  <p>1) Feed a number of negative data to the classifier
  Pros: Immediate negative data to teach the classifier
  Cons: As the number of articles I like increase, the negative data effect on the classifier dims out</p>
  
  <p>2) Turn the "neutral" data into negative data
  Pros: Now I have all the positive and (new) negative data I need
  Cons: Despite the neutral data is of mild interest to me, I'd still like to get recommendations on such article, but perhaps as a less value class.</p>