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Conference Proceeding

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We sought with this workshop, to foster a discussion of various topics that fall under the general umbrella of responsible recommendation: ethical considerations in recommendation, bias and discrimination in recommender systems, transparency and accountability, social impact of recommenders, user privacy, and other related concerns. Our goal was to encourage the community to think about how we build and study recommender systems in a socially-responsible manner.

Recommendation systems are increasingly impacting people's decisions in different walks of life including commerce, employment, dating, health, education and governance. As the impact and scope of recommendations increase, developing systems that tackle issues of fairness, transparency and accountability becomes important. This workshop was held in the spirit of FATML (Fairness, Accountability, and Transparency in Machine Learning), DAT (Data and Algorithmic Transparency), and similar workshops in related communities. With "Responsible Recommendation", we brought that conversation to RecSys.


More information about individual papers can be found at

Copyright Statement

Minimal Data.pdf (681 kB)
Towards Minimal Necessary Data: The Case for Analyzing Training Data Requirements of Recommender Algorithms

information_segregation_FATREC.pdf (1236 kB)
On Quantifying Knowledge Segregation in Society

Balanced Neighborhoods.pdf (579 kB)
Balanced Neighborhoods for Fairness-Aware Collaborative Recommendation

Impact on Crowdsourcing.pdf (767 kB)
Impact of Task Recommendation Systems in Crowdsourcing Platforms

Price of Fairness.pdf (585 kB)
Impact of Task Recommendation Systems in Crowdsourcing Platforms

fair_sharing_FATREC.pdf (548 kB)
Fair Sharing for Sharing Economy Platforms

Exploring Explanations.pdf (874 kB)
Exploring Explanations for Matrix Factorization Recommender Systems

Do Consumers Want Explanations.pdf (6897 kB)
Do News Consumers Want Explanations for Personalized News Rankings?

tintarev_fatrec17.pdf (415 kB)
Presenting Challenging Recommendations: Making Diverse News Acceptable

Academic Performance.pdf (602 kB)
Academic performance prediction in a gender-imbalanced environment

Find-Good-Items.pdf (831 kB)
Consideration on Recommendation Independence for a Find-Good-Items Task