What are the most efficient algorithms to recommend items to groups of users?

Using collaborative filtering usually applies to giving ratings to an individual user, but how would these algorithms change when nee开发者_运维百科ding to recommend an item(s) to multiple people (for example: friends wanting to watch a movie or wanting to choose a holiday together)?

Since this question is at a very general level, I will answer it at that level.

The key change is that a loss function that is typically minimized (or an objective function is maximized) for an individual would be minimized for a set. Unless you have training data for sets, this tends to be very difficult. What's more, the set could change depending on the recommendation.

Nonetheless, a naive approach would be to suggest a least common denominator item: one that, on average, maximizes the objective function.