More and more systems allow user personalization and provide item recommendations, intended to fit individual user interests. In a traditional VoD system, for example, the recommendations are oriented towards a single user even though he is not watching the video alone. Hence, there is a need to have recommendations for a set of users, a group. Collaborative filtering techniques are traditionally used to make a recommendation for a single user. Usage traces or user ratings are used to deduce their profile and to select an appropriate recommendation that way. Performing recommendation for groups is considerably more difficult because the retrieval of a group's traces of usage or ratings is complicated. As the individual profile for each member of the group is usually available, the recommendation for a group can be based on these individual profiles. This paper explores this approach and is the first step of the construction of a software toolkit for computing recommendations in function of the group composition and the chosen strategies.