Building a model and personalizing a news stream on a per-user basis is entirely feasible today. Even with very simple ML, you can see some very good real world

Building a model and personalizing a news stream on a per-user basis is entirely feasible today. Even with very simple ML, you can see some very good real world results. To avoid the problem of not being able to surface new topics because the user model has converged somewhere, you can use a bandit approach(seriously, who comes up with these names?). By that we mean that you do a bit of exploration by showing news topics drawn at random from some distribution and then adapt your model to what you learned. The naive implementation is "Every random(10,20) news items suggested by the recommender, display 1 drawn at random from the entire corpus. Adapt_Model(user_clicked)" More info: http://en.wikipedia.org/wiki/Multi-armed_bandit http://en.wikipedia.org/wiki/Multi-armed_bandit