Recommendation system - using different metrics
Recommender systems in the land of research generally work on a scale of 1 - 5. It's quite nice to get such an explicit signal from a user. However I'd imagine the reality is that most users of your system would never actually give a rating, in which case you have nothing to work with. 
Therefore I'd track page views but also try and incorporate some explicit feedback mechanism (1-5, thumbs up or down etc.)
Your algorithm will have to take this into consideration.
I'm looking to implement an item-based news recommendation system. There are several ways I want to track a user's interest in a news item; they include: rating (1-5), favorite, click-through, and time spent on news item.
My question: what are some good methods to use these different metrics for the recommendation system? Maybe merge and normalize them in some way?