Explaining content-based recommendations with topic models

Gustavo Padilha Polleti, Fabio Gagliardi Cozman

Research output: Contribution to conferenceConference Paper


© 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.
Original languageAmerican English
Number of pages6
StatePublished - 1 Oct 2019
Externally publishedYes
EventProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019 -
Duration: 1 Oct 2019 → …


ConferenceProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019
Period1/10/19 → …


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