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

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

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

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

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Fulltext (public)

arXiv:2102.09388.pdf
(Preprint), 3MB

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Citation

Ghazimatin, A., Pramanik, S., Saha Roy, R., & Weikum, G. (2021). ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models. Retrieved from https://arxiv.org/abs/2102.09388.


Cite as: http://hdl.handle.net/21.11116/0000-0008-0309-B
Abstract
System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.