rv you're dumb: identifying discarded work in Wiki article history. - Michael D. Ekstrand; John Riedl (Int. Sym. Wikis, 2009)

Automatically building research reading lists. - Michael D. Ekstrand; Praveen Kannan; James A. Stemper; John T. Butler; Joseph A. Konstan; John Riedl (RecSys, 2010)

Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. - Michael D. Ekstrand; Michael Ludwig; Joseph A. Konstan; John Riedl (RecSys, 2011)

LensKit: a modular recommender framework. - Michael D. Ekstrand; Michael Ludwig; Jack Kolb; John Riedl (RecSys, 2011)

UCERSTI 2: second workshop on user-centric evaluation of recommender systems and their interfaces. - Martijn C. Willemsen; Dirk G. F. M. Bollen; Michael D. Ekstrand (RecSys, 2011)

Searching for software learning resources using application context. - Michael D. Ekstrand; Wei Li 002; Tovi Grossman; Justin Matejka; George W. Fitzmaurice (UIST, 2011)

Collaborative Filtering Recommender Systems. - Michael D. Ekstrand; John Riedl; Joseph A. Konstan (Found. Trends Hum. Comput. Interact., 2011)

RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures. - Justin J. Levandoski; Michael D. Ekstrand; Michael Ludwig; Ahmed Eldawy; Mohamed F. Mokbel; John Riedl (Proc. VLDB Endow., 2011)

RecStore: an extensible and adaptive framework for online recommender queries inside the database engine. - Justin J. Levandoski; Mohamed Sarwat; Mohamed F. Mokbel; Michael D. Ekstrand (EDBT, 2012)

When recommenders fail: predicting recommender failure for algorithm selection and combination. - Michael D. Ekstrand; John Riedl (RecSys, 2012)

How many bits per rating? - Daniel Kluver; Tien T. Nguyen; Michael D. Ekstrand; Shilad Sen; John Riedl (RecSys, 2012)

Rating support interfaces to improve user experience and recommender accuracy. - Tien T. Nguyen; Daniel Kluver; Ting-Yu Wang; Pik-Mai Hui; Michael D. Ekstrand; Martijn C. Willemsen; John Riedl (RecSys, 2013)

User perception of differences in recommender algorithms. - Michael D. Ekstrand; F. Maxwell Harper; Martijn C. Willemsen; Joseph A. Konstan (RecSys, 2014)

Letting Users Choose Recommender Algorithms: An Experimental Study. - Michael D. Ekstrand; Daniel Kluver; F. Maxwell Harper; Joseph A. Konstan (RecSys, 2015)

Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. - Joseph A. Konstan; J. D. Walker; D. Christopher Brooks; Keith Brown; Michael D. Ekstrand (ACM Trans. Comput. Hum. Interact., 2015)

Behaviorism is Not Enough: Better Recommendations through Listening to Users. - Michael D. Ekstrand; Martijn C. Willemsen (RecSys, 2016)

Dependency Injection with Static Analysis and Context-Aware Policy. - Michael D. Ekstrand; Michael Ludwig (J. Object Technol., 2016)

Recommender Response to Diversity and Popularity Bias in User Profiles. - Sushma Channamsetty; Michael D. Ekstrand (FLAIRS, 2017)

Sturgeon and the Cool Kids: Problems with Random Decoys for Top-N Recommender Evaluation. - Michael D. Ekstrand; Vaibhav Mahant (FLAIRS, 2017)

The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users. - Michael D. Ekstrand; Maria Soledad Pera (RecSys Posters, 2017)

FATREC Workshop on Responsible Recommendation. - Michael D. Ekstrand; Amit Sharma 007 (RecSys, 2017)

Privacy for All: Ensuring Fair and Equitable Privacy Protections. - Michael D. Ekstrand; Rezvan Joshaghani; Hoda Mehrpouyan (FAT, 2018)

All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. - Michael D. Ekstrand; Mucun Tian; Ion Madrazo Azpiazu; Jennifer D. Ekstrand; Oghenemaro Anuyah; David McNeill; Maria Soledad Pera (FAT, 2018)

Exploring author gender in book rating and recommendation. - Michael D. Ekstrand; Mucun Tian; Mohammed R. Imran Kazi; Hoda Mehrpouyan; Daniel Kluver (RecSys, 2018)

2nd FATREC workshop: responsible recommendation. - Toshihiro Kamishima; Pierre-Nicolas Schwab; Michael D. Ekstrand (RecSys, 2018)

UMAP 2018 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2018) Chairs' Welcome & Organization: Preface. - Bamshad Mobasher; Robin Burke; Michael D. Ekstrand; Bettina Berendt (UMAP (Adjunct Publication), 2018)

The LKPY Package for Recommender Systems Experiments: Next-Generation Tools and Lessons Learned from the LensKit Project. - Michael D. Ekstrand (CoRR, 2018)

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442). - Nicola Ferro 001; Norbert Fuhr; Gregory Grefenstette; Joseph A. Konstan; Pablo Castells; Elizabeth M. Daly; Thierry Declerck; Michael D. Ekstrand; Werner Geyer; Julio Gonzalo; Tsvi Kuflik; Krister Lindén; et al. (Dagstuhl Manifestos, 2018)

The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. - Nicola Ferro 001; Norbert Fuhr; Gregory Grefenstette; Joseph A. Konstan; Pablo Castells; Elizabeth M. Daly; Thierry Declerck; Michael D. Ekstrand; Werner Geyer; Julio Gonzalo; Tsvi Kuflik; Krister Lindén; et al. (SIGIR Forum, 2018)

Rating-Based Collaborative Filtering: Algorithms and Evaluation. - Daniel Kluver; Michael D. Ekstrand; Joseph A. Konstan (Social Information Access, 2018)

Fairness and discrimination in recommendation and retrieval. - Michael D. Ekstrand; Robin Burke; Fernando Diaz 001 (RecSys, 2019)

StoryTime: eliciting preferences from children for book recommendations. - Ashlee Milton; Michael Green; Adam Keener; Joshua Ames; Michael D. Ekstrand; Maria Soledad Pera (RecSys, 2019)

Fairness and Discrimination in Retrieval and Recommendation. - Michael D. Ekstrand; Robin Burke; Fernando Diaz 001 (SIGIR, 2019)

Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval (FACTS-IR). - Alexandra Olteanu; Jean Garcia-Gathright; Maarten de Rijke; Michael D. Ekstrand (SIGIR, 2019)

FairUMAP 2019 Chairs' Welcome Overview. - Bettina Berendt; Veronika Bogina; Robin Burke; Michael D. Ekstrand; Alan Hartman; Styliani Kleanthous; Tsvi Kuflik; Bamshad Mobasher; Jahna Otterbacher (UMAP (Adjunct Publication), 2019)

Recommender Systems Notation: Proposed Common Notation for Teaching and Research. - Michael D. Ekstrand; Joseph A. Konstan (CoRR, 2019)

Proceedings of FACTS-IR 2019. - Alexandra Olteanu; Jean Garcia-Gathright; Maarten de Rijke; Michael D. Ekstrand (CoRR, 2019)

FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. - Alexandra Olteanu; Jean Garcia-Gathright; Maarten de Rijke; Michael D. Ekstrand; Adam Roegiest; Aldo Lipani; Alex Beutel; Ana Lucic; Ana-Andreea Stoica; Anubrata Das 001; Asia Biega; Bart Voorn; et al. (SIGIR Forum, 2019)

Estimating Error and Bias in Offline Evaluation Results. - Mucun Tian; Michael D. Ekstrand (CHIIR, 2020)

Evaluating Stochastic Rankings with Expected Exposure. - Fernando Diaz 001; Bhaskar Mitra 001; Michael D. Ekstrand; Asia J. Biega; Ben Carterette (CIKM, 2020)

LensKit for Python: Next-Generation Software for Recommender Systems Experiments. - Michael D. Ekstrand (CIKM, 2020)

3rd FAccTRec Workshop: Responsible Recommendation. - Michael D. Ekstrand; Pierre-Nicolas Schwab; Jean Garcia-Gathright; Toshihiro Kamishima; Nasim Sonboli (RecSys, 2020)

UMAP 2020 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2020) Chairs' Welcome. - Bamshad Mobasher; Styliani Kleanthous; Bettina Berendt; Michael D. Ekstrand; Jahna Otterbacher; Avital Shulner-Tal (UMAP (Adjunct Publication), 2020)

FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization. - Bamshad Mobasher; Styliani Kleanthous; Michael D. Ekstrand; Bettina Berendt; Jahna Otterbacher; Avital Shulner-Tal (UMAP, 2020)

Enhancing classroom instruction with online news. - Michael D. Ekstrand; Katherine Landau Wright; Maria Soledad Pera (Aslib J. Inf. Manag., 2020)

Overview of the TREC 2019 Fair Ranking Track. - Asia J. Biega; Fernando Diaz 001; Michael D. Ekstrand; Sebastian Kohlmeier (CoRR, 2020)

Comparing Fair Ranking Metrics. - Amifa Raj; Connor Wood; Ananda Montoly; Michael D. Ekstrand (CoRR, 2020)

SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research. - Michael D. Ekstrand; Allison Chaney; Pablo Castells; Robin Burke; David Rohde; Manel Slokom (RecSys, 2021)

FAccTRec 2021: The 4th Workshop on Responsible Recommendation. - Michael D. Ekstrand; Pierre-Nicolas Schwab; Toshihiro Kamishima; Nasim Sonboli (RecSys, 2021)

Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse. - Ngozi Ihemelandu; Michael D. Ekstrand (Perspectives@RecSys, 2021)

Baby Shark to Barracuda: Analyzing Children's Music Listening Behavior. - Lawrence Spear; Ashlee Milton; Garrett Allen; Amifa Raj; Michael Green; Michael D. Ekstrand; Maria Soledad Pera (RecSys, 2021)

Overview of the TREC 2021 Fair Ranking Track. - Michael D. Ekstrand; Amifa Raj; Graham McDonald; Isaac Johnson 001 (TREC, 2021)

Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans. - A. K. M. Nuhil Mehdy; Michael D. Ekstrand; Bart P. Knijnenburg; Hoda Mehrpouyan (UMAP, 2021)

Estimation of Fair Ranking Metrics with Incomplete Judgments. - Ömer Kirnap; Fernando Diaz 001; Asia Biega; Michael D. Ekstrand; Ben Carterette; Emine Yilmaz (WWW, 2021)

Fairness and Discrimination in Information Access Systems. - Michael D. Ekstrand; Anubrata Das 001; Robin Burke; Fernando Diaz 001 (CoRR, 2021)

Pink for Princesses, Blue for Superheroes: The Need to Examine Gender Stereotypes in Kid's Products in Search and Recommendations. - Amifa Raj; Ashlee Milton; Michael D. Ekstrand (CoRR, 2021)

Overview of the TREC 2020 Fair Ranking Track. - Asia J. Biega; Fernando Diaz 001; Michael D. Ekstrand; Sergey Feldman; Sebastian Kohlmeier (CoRR, 2021)

Multiversal Simulacra: Understanding Hypotheticals and Possible Worlds Through Simulation. - Michael D. Ekstrand (CoRR, 2021)

Preface to the special issue on fair, accountable, and transparent recommender systems. - Robin Burke; Michael D. Ekstrand; Nava Tintarev; Julita Vassileva (User Model. User Adapt. Interact., 2021)

Exploring author gender in book rating and recommendation. - Michael D. Ekstrand; Daniel Kluver (User Model. User Adapt. Interact., 2021)

Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison. - Amifa Raj; Michael D. Ekstrand (SIGIR, 2022)

Overview of the TREC 2022 Fair Ranking Track. - Michael D. Ekstrand; Graham McDonald; Amifa Raj; Isaac Johnson 001 (TREC, 2022)

The Multisided Complexity of Fairness in Recommender Systems. - Nasim Sonboli; Robin Burke; Michael D. Ekstrand; Rishabh Mehrotra (AI Mag., 2022)

Fire Dragon and Unicorn Princess; Gender Stereotypes and Children's Products in Search Engine Responses. - Amifa Raj; Michael D. Ekstrand (CoRR, 2022)

Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. - Jonathan Stray; Alon Y. Halevy; Parisa Assar; Dylan Hadfield-Menell; Craig Boutilier; Amar Ashar; Lex Beattie; Michael D. Ekstrand; Claire Leibowicz; Connie Moon Sehat; Sara Johansen; Lianne Kerlin; et al. (CoRR, 2022)

Matching Consumer Fairness Objectives & Strategies for RecSys. - Michael D. Ekstrand; Maria Soledad Pera (CoRR, 2022)

Fairness in Information Access Systems. - Michael D. Ekstrand; Anubrata Das 001; Robin Burke; Fernando Diaz 001 (Found. Trends Inf. Retr., 2022)

Fairness in Recommender Systems. - Michael D. Ekstrand; Anubrata Das 001; Robin Burke; Fernando Diaz 001 (Recommender Systems Handbook, 2022)

Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. - Christine Pinney; Amifa Raj; Alex Hanna; Michael D. Ekstrand (CHIIR, 2023)

FAccTRec 2023: The 6th Workshop on Responsible Recommendation. - Michael D. Ekstrand; Jean Garcia-Gathright; Nasim Sonboli; Amifa Raj; Karlijn Dinnissen (RecSys, 2023)

Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit. - Tobias Vente; Michael D. Ekstrand; Joeran Beel (RecSys, 2023)

Inference at Scale: Significance Testing for Large Search and Recommendation Experiments. - Ngozi Ihemelandu; Michael D. Ekstrand (SIGIR, 2023)

Patterns of Gender-Specializing Query Reformulation. - Amifa Raj; Bhaskar Mitra 001; Nick Craswell; Michael D. Ekstrand (SIGIR, 2023)

Candidate Set Sampling for Evaluating Top-N Recommendation. - Ngozi Ihemelandu; Michael D. Ekstrand (WI/IAT, 2023)

Towards Measuring Fairness in Grid Layout in Recommender Systems. - Amifa Raj; Michael D. Ekstrand (CoRR, 2023)

Unified Browsing Models for Linear and Grid Layouts. - Amifa Raj; Michael D. Ekstrand (CoRR, 2023)

Responsible AI Research Needs Impact Statements Too. - Alexandra Olteanu; Michael D. Ekstrand; Carlos Castillo 001; Jina Suh (CoRR, 2023)

Seeking Information with a More Knowledgeable Other. - Michael D. Ekstrand; Maria Soledad Pera; Katherine Landau Wright (Interactions, 2023)

Not Just Algorithms: Strategically Addressing Consumer Impacts in Information Retrieval. - Michael D. Ekstrand; Lex Beattie; Maria Soledad Pera; Henriette Cramer (ECIR (4), 2024)

Multiple Testing for IR and Recommendation System Experiments. - Ngozi Ihemelandu; Michael D. Ekstrand (ECIR (3), 2024)

Towards Optimizing Ranking in Grid-Layout for Provider-Side Fairness. - Amifa Raj; Michael D. Ekstrand (ECIR (5), 2024)

Conducting Recommender Systems User Studies Using POPROX. - Robin Burke; Joseph A. Konstan; Michael D. Ekstrand (RecSys, 2024)

FAccTRec 2024: The 7th Workshop on Responsible Recommendation. - Michael D. Ekstrand; Toshihiro Kamishima; Amifa Raj; Karlijn Dinnissen (RecSys, 2024)

AltRecSys: A Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation. - Michael D. Ekstrand; Maria Soledad Pera; Alan Said (RecSys, 2024)

It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation. - Andres Ferraro; Michael D. Ekstrand; Christine Bauer 001 (RecSys, 2024)

The Impossibility of Fair LLMs. - Jacy Reese Anthis; Kristian Lum; Michael D. Ekstrand; Avi Feller; Alexander D'Amour; Chenhao Tan (CoRR, 2024)

Distributionally-Informed Recommender System Evaluation. - Michael D. Ekstrand; Ben Carterette; Fernando Diaz 001 (Trans. Recomm. Syst., 2024)

Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. - Jonathan Stray; Alon Y. Halevy; Parisa Assar; Dylan Hadfield-Menell; Craig Boutilier; Amar Ashar; Chloé Bakalar; Lex Beattie; Michael D. Ekstrand; Claire Leibowicz; Connie Moon Sehat; Sara Johansen; et al. (Trans. Recomm. Syst., 2024)

The Impossibility of Fair LLMs. - Jacy Reese Anthis; Kristian Lum; Michael D. Ekstrand; Avi Feller; Chenhao Tan (ACL (1), 2025)

Fairness in Information Access Conceptual Foundations and New Directions. - Michael D. Ekstrand (ECIR (5), 2025)

User and Recommender Behavior Over Time: Contextualizing Activity Effectiveness Diversity and Fairness in Book Recommendation. - Samira Vaez Barenji; Sushobhan Parajuli; Michael D. Ekstrand (UMAP (Adjunct Publication), 2025)

Conducting Recommender Systems User Studies Using POPROX. - Robin Burke; Michael D. Ekstrand (UMAP (Adjunct Publication), 2025)

The Evolving Landscape of Youth Online Safety: Insights from News Media Analysis. - Mohammad Namvarpour; Elham Aghakhani; Michael D. Ekstrand; Rezvaneh Rezapour; Afsaneh Razi (WebSci, 2025)

Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor. - Alexandra Olteanu; Su Lin Blodgett; Agathe Balayn; Angelina Wang; Fernando Diaz 001; Flávio du Pin Calmon; Margaret Mitchell; Michael D. Ekstrand; Reuben Binns; Solon Barocas (CoRR, 2025)

Recommending With, Not For: Co-Designing Recommender Systems for Social Good. - Michael D. Ekstrand; Afsaneh Razi; Aleksandra Sarcevic; Maria Soledad Pera; Robin Burke; Katherine Landau Wright (CoRR, 2025)