Explaining content-based recommendations with topic models

Gustavo Padilha Polleti, Fabio Gagliardi Cozman

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

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 originalInglés estadounidense
Páginas800-805
Número de páginas6
DOI
EstadoPublicada - 1 oct 2019
Publicado de forma externa
EventoProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019 -
Duración: 1 oct 2019 → …

Conferencia

ConferenciaProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019
Período1/10/19 → …

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    Padilha Polleti, G., & Gagliardi Cozman, F. (2019). Explaining content-based recommendations with topic models. 800-805. Papel presentado en Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019, . https://doi.org/10.1109/BRACIS.2019.00143