In this paper we report the results of the experiment carried out by our institutions for participating in the track 1 of the the 2011 CAMRa challenge workshop. We ran some variations of a traditional user-based neighborhood recommender system based on two simple ideas: (1) Force the inclusion of household members into the neighborhood of the user and (2) increase the similarity of users that use the system if they use the system at similar time slots. The approaches are evaluated using the MAP, P@5, P@10 and AUC metrics. Results show that a small improvement is achieved on of the chosen metrics when comparing the social and time strategies to a traditional KNN approach.