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We propose a novel hybrid recommendation approach to address the well-known cold-start problem in Collaborative Filtering (CF). Our approach makes use of Cross-Level Association RulEs (CLARE) to integrate content information about domain items into collaborative filters. We first introduce a preference model comprising both user–item and item–item relationships in recommender systems, and present a motivating example of our work based on the model. We then describe how CLARE generates cold-start recommendations. We empirically evaluated the effectiveness of CLARE, which shows superior performance to related work in addressing the cold-start problem.