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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Empowering Recommendation Technologies Through Argumentation
verfasst von : CarlosIván Chesñevar, Ana Gabriela Maguitman, María Paula González
Erschienen in: Argumentation in Artificial Intelligence
Verlag: Springer US
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User support systems have evolved in the last years as specialized tools to assist users in a plethora of computer-mediated tasks by providing guidelines or hints 19. Recommender systems are a special class of user support tools that act in cooperation with users, complementing their abilities and augmenting their performance by offering proactive or on-demand, context-sensitive support. Recommender systems are mostly based on machine learning and information retrieval algorithms, providing typically suggestions based on quantitative evidence (i.e. measures of similarity between objects or users). The inference process which led to such suggestions is mostly unknown (i.e. ‘black-box’ metaphor). Although the effectiveness of existing recommenders is remarkable, they still have some serious limitations.