A Taxonomy for Generating Explanations in Recommender Systems

A Taxonomy for Generating Explanations in Recommender Systems. Friedrich, G. & Zanker, M. AI Magazine, 2011.

In recommender systems, explanations serve as an additional type of information that can help users to better understand the system's output and promote objectives such as trust, confidence in decision making or utility. This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified. Finally, the authors present their view on open research issues and opportunities for future work on this topic.

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 notes = {Categorías propuestas para clasificación de explicaciones:<br/>-Modelo de razonamiento. Si está basada la explicación en la recomendación o no. White box: la explicación se basa en la recomendación y Black Box: se desconoce el proceso de recomendación y se aplican otras técnicas para generar la explicación.<br/>-Paradigma utilizado en la explicación: colaborativo (basado en relaciones entre usuarios), basado en conocimiento (basado en las preferencias de usuarios y relaciones entre items y usuarios), basado en contenido (basado en las descripciones de los items).<br/>-Otras categorías menos importantes: <br/>*Modelo de usuario: las explicaciones están centradas en el usuario.<br/>*Recomendación de item: las explicaciones están centradas en el item que se recomienda<br/>*Alternativas: si las explicaciones argumentan en favor o en contra de alternativas al item recomendado.},
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 abstract = {<p>In recommender systems, explanations serve as an additional type of information that can help users to better understand the system's output and promote objectives such as trust, confidence in decision making or utility. This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified. Finally, the authors present their view on open research issues and opportunities for future work on this topic.</p>},
 bibtype = {article},
 author = {Friedrich, Gerhard and Zanker, Markus},
 journal = {AI Magazine}
}

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