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In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. 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The trend is now clear that future consumer computing experience will be based on multiple pervasive communication devices and services, where navigability, context-sensitivity, adaptability and ubiquity are key characteristics. Several issues have been studied, models and methodologies proposed, and tools and systems implemented. However, we look at the foundation, where some of the most relevant issues probably are a formal model of context-awareness and context-dependency. 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