Abstract
© 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 language | American English |
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Pages | 800-805 |
Number of pages | 6 |
DOIs | |
State | Published - 1 Oct 2019 |
Externally published | Yes |
Event | Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019 - Duration: 1 Oct 2019 → … |
Conference
Conference | Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019 |
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Period | 1/10/19 → … |