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.
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Acknowledgments
This research was funded by Agencia Nacional de Promoción Científica y Tecnológica (PICT 2005 - 32373), by CONICET (Argentina), by Projects TIN2006-15662-C02-01 and TIN2008-06596-C02-01 (MEC, Spain), and PGI Projects 24/ZN10, 24/N023 and 24/N020 (SGCyT, Universidad Nacional del Sur, Argentina).
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Chesñevar, C., Maguitman, A.G., González, M.P. (2009). Empowering Recommendation Technologies Through Argumentation. In: Simari, G., Rahwan, I. (eds) Argumentation in Artificial Intelligence. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-98197-0_20
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