Interactivity, Fairness and Explanations in Recommendations-Reference-Cited by-同舟云学术

Interactivity, Fairness and Explanations in Recommendations

Published:2021-06-21 Issue: Volume: Page:
ISSN:
Container-title:Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
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Author:

Giannopoulos Giorgos¹,Papastefanatos George¹,Sacharidis Dimitris²,Stefanidis Kostas³

Affiliation:

1. IMSI/Athena Research Center, Greece

2. Université Libre de Bruxelles, Belgium

3. Tampere University, Finland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3450614.3462238

Reference35 articles.

1. Nikos Bikakis John Liagouris Maria Krommyda George Papastefanatos and Timos K. Sellis. 2016. graphVizdb: A scalable platform for interactive large graph visualization. In ICDE. Nikos Bikakis John Liagouris Maria Krommyda George Papastefanatos and Timos K. Sellis. 2016. graphVizdb: A scalable platform for interactive large graph visualization. In ICDE.

2. In-situ visual exploration over big raw data

3. Rodrigo Borges and Kostas Stefanidis. 2019. Enhancing Long Term Fairness in Recommendations with Variational Autoencoders. In MEDES. Rodrigo Borges and Kostas Stefanidis. 2019. Enhancing Long Term Fairness in Recommendations with Variational Autoencoders. In MEDES.

4. Rodrigo Borges and Kostas Stefanidis. 2020. On Measuring Popularity Bias in Collaborative Filtering Data. In BigVis. Rodrigo Borges and Kostas Stefanidis. 2020. On Measuring Popularity Bias in Collaborative Filtering Data. In BigVis.

5. Rodrigo Borges and Kostas Stefanidis. 2020. On Mitigating Popularity Bias in Recommendations via Variational Autoencoders. In SAC. Rodrigo Borges and Kostas Stefanidis. 2020. On Mitigating Popularity Bias in Recommendations via Variational Autoencoders. In SAC.

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