short-paper

Interactivity, Fairness and Explanations in Recommendations

Authors:

Giorgos Giannopoulos,

George Papastefanatos,

Dimitris Sacharidis,

Kostas StefanidisAuthors Info & Claims

UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

Pages 157 - 161

https://doi.org/10.1145/3450614.3462238

Published: 22 June 2021 Publication History

Abstract

More and more aspects of our everyday lives are influenced by automated decisions made by systems that statistically analyze traces of our activities. It is thus natural to question whether such systems are trustworthy, particularly given the opaqueness and complexity of their internal workings. In this paper, we present our ongoing work towards a framework that aims to increase trust in machine-generated recommendations by combining ideas from three separate recent research directions, namely explainability, fairness and user interactive visualization. The goal is to enable different stakeholders, with potentially varying levels of background and diverse needs, to query, understand, and fix sources of distrust.

References

[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.

[2]

Nikos Bikakis, Stavros Maroulis, George Papastefanatos, and Panos Vassiliadis. 2021. In-situ visual exploration over big raw data. Inf. Syst. 95, 101616. https://doi.org/10.1016/j.is.2020.101616

[3]

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.

[5]

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

[6]

Robin Burke. 2017. Multisided Fairness for Recommendation. CoRR abs/1707.00093(2017).

[7]

Fatih Gedikli, Dietmar Jannach, and Mouzhi Ge. 2014. How should I explain? A comparison of different explanation types for recommender systems. Int. J. Hum. Comput. Stud. 72, 4 (2014).

Digital Library

[8]

Bryce Goodman and Seth R. Flaxman. 2017. European Union Regulations on Algorithmic Decision-Making and a ”Right to Explanation”. AI Magazine 38, 3 (2017), 50–57.

[9]

Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl. 2000. Explaining collaborative filtering recommendations. In CSCW.

[10]

Aylin Caliskan Islam, Joanna J. Bryson, and Arvind Narayanan. 2016. Semantics derived automatically from language corpora necessarily contain human biases. CoRR abs/1608.07187(2016).

[11]

Uwe Jugel, Zbigniew Jerzak, Gregor Hackenbroich, and Volker Markl. 2016. VDDA: automatic visualization-driven data aggregation in relational databases. VLDB J. 25, 1 (2016), 53–77.

Digital Library

[12]

Vassilis Kaffes, Dimitris Sacharidis, and Giorgos Giannopoulos. 2021. Model-Agnostic Counterfactual Explanations of Recommendations. In UMAP.

[13]

Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2018. Recommendation Independence. In FAT.

[14]

Bart P. Knijnenburg, Svetlin Bostandjiev, John O’Donovan, and Alfred Kobsa. 2012. Inspectability and control in social recommenders. In RecSys.

[15]

Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva. 2017. Counterfactual Fairness. In NIPS.

[16]

Yuyu Luo, Xuedi Qin, Nan Tang, and Guoliang Li. 2018. DeepEye: Towards Automatic Data Visualization. In ICDE.

[17]

Stavros Maroulis, Nikos Bikakis, George Papastefanatos, and Panos Vassiliadis. 2021. RawVis: A System for Efficient In-situ Visual Analytics. In SIGMOD (Demo).

[18]

Stavros Maroulis, Nikos Bikakis, George Papastefanatos, Panos Vassiliadis, and Yannis Vassiliou. 2021. Adaptive Indexing for In-situ Visual Exploration and Analytics. In DOLAP.

[19]

Dominik Moritz, Danyel Fisher, Bolin Ding, and Chi Wang. 2017. Trust, but Verify: Optimistic Visualizations of Approximate Queries for Exploring Big Data. In CHI.

[20]

Evaggelia Pitoura, Georgia Koutrika, and Kostas Stefanidis. 2020. Fairness in Rankings and Recommenders. In EDBT.

[21]

Evaggelia Pitoura, Kostas Stefanidis, and Georgia Koutrika. 2021. Fairness in Rankings and Recommendations: An Overview. CoRR abs/2104.05994(2021). https://arxiv.org/abs/2104.05994

[22]

Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. ”Why Should I Trust You?”: Explaining the Predictions of Any Classifier. In KDD.

[23]

Dimitris Sacharidis. 2019. Top-N group recommendations with fairness. In SAC.

[24]

Dimitris Sacharidis, Kyriakos Mouratidis, and Dimitrios Kleftogiannis. 2019. A Common Approach for Consumer and Provider Fairness in Recommendations. In RecSys Late-Breaking Results.

[25]

Babak Salimi, Luke Rodriguez, Bill Howe, and Dan Suciu. 2019. Interventional Fairness: Causal Database Repair for Algorithmic Fairness. In SIGMOD.

Digital Library

[26]

Julia Stoyanovich, Serge Abiteboul, and Gerome Miklau. 2016. Data Responsibly: Fairness, Neutrality and Transparency in Data Analysis. In EDBT.

[27]

Maria Stratigi, Jyrki Nummenmaa, Evaggelia Pitoura, and Kostas Stefanidis. 2020. Fair sequential group recommendations. In SAC.

[28]

Maria Stratigi, Katerina Tzompanaki, and Kostas Stefanidis. 2020. Why-Not Questions & Explanations for Collaborative Filtering. In WISE(Lecture Notes in Computer Science).

[29]

Nava Tintarev and Judith Masthoff. 2007. A Survey of Explanations in Recommender Systems. In ICDEW.

[30]

Nava Tintarev and Judith Masthoff. 2012. Evaluating the effectiveness of explanations for recommender systems - Methodological issues and empirical studies on the impact of personalization. User Model. User-Adapt. Interact. 22, 4-5 (2012), 399–439.

Digital Library

[31]

Georgia Troullinou, Haridimos Kondylakis, Kostas Stefanidis, and Dimitris Plexousakis. 2018. Exploring RDFS KBs Using Summaries. In ISWC.

[32]

Jesse Vig, Shilad Sen, and John Riedl. 2009. Tagsplanations: explaining recommendations using tags. In IUI.

[33]

Sandra Wachter, Brent D. Mittelstadt, and Chris Russell. 2017. Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR. CoRR abs/1711.00399(2017).

[34]

Jing Nathan Yan, Ziwei Gu, Hubert Lin, and Jeffrey M. Rzeszotarski. 2020. Silva: Interactively Assessing Machine Learning Fairness Using Causality. In CHI.

[35]

Ziwei Zhu, Xia Hu, and James Caverlee. 2018. Fairness-Aware Tensor-Based Recommendation. In CIKM.

Cited By

Verma SBoonsanong VHoang MHines KDickerson JShah C(2024)Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A ReviewACM Computing Surveys10.1145/367711956:12(1-42)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3677119
Laine JMinkkinen MMäntymäki M(2024)Ethics-based AI auditingInformation and Management10.1016/j.im.2024.10396961:5Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.im.2024.103969
Leal FVeloso BMalheiro BBurguillo J(2023)Towards adaptive and transparent tourism recommendations: A surveyExpert Systems10.1111/exsy.13400Online publication date: 18-Jul-2023
https://doi.org/10.1111/exsy.13400

Index Terms

Interactivity, Fairness and Explanations in Recommendations
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems

Index terms have been assigned to the content through auto-classification.

Recommendations

Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics
UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

Beyond accuracy metrics, such as fairness and diversity, have become widely studied topics in recommender systems. Improving these metrics is important not only from an ethical and legal perspective, but can also improve overall user satisfaction. ...
Naïve filterbots for robust cold-start recommendations
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

The goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...
Calibrated recommendations
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

When a user has watched, say, 70 romance movies and 30 action movies, then it is reasonable to expect the personalized list of recommended movies to be comprised of about 70% romance and 30% action movies as well. This important property is known as ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

June 2021

431 pages

ISBN:9781450383677

DOI:10.1145/3450614

Editors:
Judith Masthoff
Utrecht University, The Netherlands and University of Aberdeen, UK
,
Eelco Herder
Radboud University, The Netherlands
,
Nava Tintarev
University of Maastricht, The Netherlands
,
Marko Tkalčič
University of Primorska, Slovenia

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

UMAP '21

Sponsor:

UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization

June 21 - 25, 2021

Utrecht, Netherlands

Acceptance Rates

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
197
Total Downloads

Downloads (Last 12 months)26
Downloads (Last 6 weeks)3

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Verma SBoonsanong VHoang MHines KDickerson JShah C(2024)Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A ReviewACM Computing Surveys10.1145/367711956:12(1-42)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3677119
Laine JMinkkinen MMäntymäki M(2024)Ethics-based AI auditingInformation and Management10.1016/j.im.2024.10396961:5Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.im.2024.103969
Leal FVeloso BMalheiro BBurguillo J(2023)Towards adaptive and transparent tourism recommendations: A surveyExpert Systems10.1111/exsy.13400Online publication date: 18-Jul-2023
https://doi.org/10.1111/exsy.13400

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents