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- W2149607072 abstract "We propose a novel hybrid recommendation algorithm for addressing the well-known cold-start problem in Collaborative Filtering (CF). Our algorithm 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 then describe how the CLARE algorithm generates recommendations for cold-start items based on the preference model. Experimental results validated that CLARE is capable of recommending cold-start items, and that it increases the number of recommendable items significantly by addressing the cold-start problem." @default.
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- W2149607072 date "2007-11-02" @default.
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- W2149607072 title "Applying Cross-Level Association Rule Mining to Cold-Start Recommendations" @default.
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- W2149607072 doi "https://doi.org/10.5555/1339264.1339676" @default.
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