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The prediction of contexts plays an important part in the field of context aware systems and environments for adapting services proactively to users' needs. To the best of our knowledge, most research literature on context prediction focused on forecasting a user's contexts only using his available context history. In the case of a user suddenly changing his behaviour in an unexpected way, the context history of the user does not provide future context information for the observed pattern. Hence context prediction algorithms will fail to forecast the appropriate future context. To overcome the gap of missing context information in the user's context history, we propose the Collaborative Context Prediction (CCP) approach. Our results show that the proposed CCP approach is able to give accurate predictions in the absence of needed context information and outperforms the Active LeZi method.