Explaining content-based recommendations with topic models

Gustavo Padilha Polleti; Fabio Gagliardi Cozman

doi:10.1109/BRACIS.2019.00143

Explaining content-based recommendations with topic models

Gustavo Padilha Polleti, Fabio Gagliardi Cozman

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Resumen

© 2019 IEEE. Recommendation systems play a key role in current online commerce enterprises. Despite their success, they usually behave like black-boxes from the user perspective, typically failing to produce high quality human-computer interactions; interpretability is thus a major concern for the next generation of recommendation systems. In this paper we propose a model-agnostic method based on topic models that generates explanations for content-based recommendation systems.

Idioma original	Inglés estadounidense
Páginas	800-805
Número de páginas	6
DOI	https://doi.org/10.1109/BRACIS.2019.00143
Estado	Publicada - 1 oct. 2019
Publicado de forma externa	Sí
Evento	Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019 - Duración: 1 oct. 2019 → …

Conferencia

Conferencia	Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019
Período	1/10/19 → …

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10.1109/BRACIS.2019.00143

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title = "Explaining content-based recommendations with topic models",

abstract = "{\textcopyright} 2019 IEEE. Recommendation systems play a key role in current online commerce enterprises. Despite their success, they usually behave like black-boxes from the user perspective, typically failing to produce high quality human-computer interactions; interpretability is thus a major concern for the next generation of recommendation systems. In this paper we propose a model-agnostic method based on topic models that generates explanations for content-based recommendation systems.",

author = "{Padilha Polleti}, Gustavo and {Gagliardi Cozman}, Fabio",

year = "2019",

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N2 - © 2019 IEEE. Recommendation systems play a key role in current online commerce enterprises. Despite their success, they usually behave like black-boxes from the user perspective, typically failing to produce high quality human-computer interactions; interpretability is thus a major concern for the next generation of recommendation systems. In this paper we propose a model-agnostic method based on topic models that generates explanations for content-based recommendation systems.

AB - © 2019 IEEE. Recommendation systems play a key role in current online commerce enterprises. Despite their success, they usually behave like black-boxes from the user perspective, typically failing to produce high quality human-computer interactions; interpretability is thus a major concern for the next generation of recommendation systems. In this paper we propose a model-agnostic method based on topic models that generates explanations for content-based recommendation systems.

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Explaining content-based recommendations with topic models

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