Joint embedding–classifier learning for interpretable collaborative filtering

Joint embedding–classifier learning for interpretable collaborative filtering

Abstract Background Interpretability is a topical question in recommender systems, especially in healthcare applications. An interpretable classifier quantifies the importance of each input feature for the predicted item-user association in a non-ambiguous fashion. Results We introduce the novel Joi...

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Bibliographic Details
Main Authors: Clémence Réda, Jill-Jênn Vie, Olaf Wolkenhauer
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Bioinformatics
Subjects:
Drug repurposing
Interpretability
Gene expression
Collaborative filtering
Online Access:https://doi.org/10.1186/s12859-024-06026-8
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https://doi.org/10.1186/s12859-024-06026-8

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