In this paper, we describe our approach to the households recommendation track of Challenge on Context-Aware Movie Recommendation(CAMRa) 2011. The challenge of the track is to generate recommendations for households. In this paper, we introduce an approach that uses time information and exponential smoothing to predict user and item ratings. We provide traditional collaborative filtering algorithms as a baseline, and show that our approach yields better results.
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