distinguish users for recommender system How can you calculate better video recommendations for a SmartTV app which is used by multiple users in a household. I don't know which user is currently watching the video because the account for the app is shared. So the current user might get a recommendation based on video usage of an other user.

I was thinking of using principal component analysis to find out how many users live in the household and which user is likely to watch the video now. I could map the usage data to this specific user and then build recommendations for this hypothetical user.

What would be the best approach to solve this problem. Are there best practices or papers which describe a solution?
I was interestedin this problem awhile back ago.  I still have this paper and should serve as a good primer.

Guess Who Rated Thos Movie:...