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However, there remains a key issue as how contextual information can be used to create intelligent and useful recommender systems. To assist the development and use of context-aware recommendation capabilities, we propose a graph-based framework to model and incorporate contextual information into the recommendation process in an advantageous way. A contextual graph-based relevance measure (CGR) is specifically designed to assess the potential relevance between the target user and the items further used to make an item recommendation. We also propose a probabilistic-based postfiltering strategy to refine the recommendation results as contextual conditions are explicitly given in a query. 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