ELIXIR: Learning from User Feedback on Explanations to Improve Recommender 
Models

Ghazimatin, Azin; Pramanik, Soumajit; Saha Roy, Rishiraj; Weikum, Gerhard

doi:10.1145/3442381.3449848

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models

MPS-Authors

/persons/resource/persons208714

Ghazimatin, Azin
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons185343

Saha Roy, Rishiraj
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons45720

Weikum, Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

3442381.3449848.pdf
(Publisher version), 3MB

Supplementary Material (public)

There is no public supplementary material available

Citation

Ghazimatin, A., Pramanik, S., Saha Roy, R., & Weikum, G. (2021). ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models. In J. Leskovec, M. Grobelnik, M. Najork, J. Tang, & L. Zia (Eds.), The Web Conference 2021 (pp. 3850-3860). New York, NY: ACM. doi:10.1145/3442381.3449848.

Cite as: https://hdl.handle.net/21.11116/0000-0008-0303-1

Abstract

There is no abstract available