Binary (Unary) Recommendation System with Biased Views
The simplest answer with the cleanest solution may be one that combines your binary click data and view data into another model feature that you can optimize against. What this looks like depends on your knowledge of your end-to-end system. 

For instance, is it "good" that a user clicks on something with a high amount of views, or better if a user clicks on something with a low amount of views? You might have very different formulas depending on this question alone: 

normalization_function((1/views) * mean(clicks)) vs perhaps normalization_function((view) * mean(clicks))

Be sure to check the assumptions of your matrix factorization implementation, but this new features may just be able to be swapped with your click data.

As a side note, I don't know exactly what you are implementing, but clicks and views generally represent different things -- as a rough guide (clicks = engagement, view = eyeballs), so it might not mean much to combine these.