Abstract
While collaborative filtering often yields very good recommendation results, in many real-world recommendation scenarios cold-start and data sparseness remain important problems. This paper presents a hybrid recommender system that integrates user demographics and item characteristics, around a collaborative filtering core based on user-item interactions. The recommender system is evaluated on Movie lens data (including genre information and user data) as well as real-world data from a discount coupon provider. We show that the inclusion of additional item and user information can have great impact on recommendation quality, especially in settings where little interaction data is available.
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