I am beginner in ML (i have done only Andrew Ng's ML course) and i have to work on news recommendation.
I went through this paper which mentions different methods used for news recommendation (at 7th page) and most of them are using some sort of probabilistic methods (bayesian networks, latent dirichlet allocation, naive bayes model, probabilistic matrix factorization models). Also, some news recommenders are based on multi-armed-bandit problem (e.g. yahoo's front page). I have zero knowledge about these methods.
I am very confused regarding what should be my next step. Right now, i am planning to go ahead with a very basic hybrid approach (with collaborative filtering and content based filtering). But it seems like i will have to explore these fields very soon, so what will be a good strategy (and resource) to explore these fields?