Many websites provide commenting facilities for users to express their opinions or sentiments with regards to content items, such as, videos, news stories, blog posts, etc. Previous studies have shown that user comments contain valuable information that can provide insight onWeb documents and may be utilized for various tasks. This work presents a model that predicts, for a given user, suitable news stories for commenting. The model achieves encouraging results regarding the ability to connect users with stories they are likely to comment on. This provides grounds for personalized recommendations of stories to users who may want to take part in their discussion. We combine a content-based approach with a collaborative-filtering approach (utilizing users'co-commenting patterns) in a latent factor modeling framework. We experiment with several variations of the model's loss function in order to adjust it to the problem domain. We evaluate the results on two datasets and show that employing co-commenting patterns improves upon using content features alone, even with as few as two available comments per story. Finally, we try to incorporate available social network data into the model. Interestingly, the social data does not lead to substantial performance gains, suggesting that the value of social data for this task is quite negligible.