When recommenders are applied to news you'll get more of the stuff you like, but you're losing out because you are not seeing the stuff you didn't know that you would like.
When recommenders are applied to news you'll get more of the stuff you like, but you're losing out because you are not seeing the stuff you didn't know that you would like.
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)"