Proceeding with various methods for news recommendation 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?
Recommender Systems are a huge topic of its own right and goes without saying, with a lot of research going on. 

This book does a deep-dive into recommender systems and may not be something you want, but it's helpful as a reference. It seems like you were unsure what those terms mean. The Berekely AI Course covers most of these topics and their lectures are available for free.

You might be able to make better choices after going through some of the above materials.