SelectedWorks - Michael D. Ekstrand

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Selected Works of Michael D. Ekstrand

Assistant Professor, Department of Computer Science

Starting at Boise State University in 2016, Dr. Michael D. Ekstrand specializes in researching human-computer interaction regarding recommender systems. Currently an assistant professor in the Department of Computer Science at Boise State University. Dr. Ekstrand received his Ph.D. in Computer Science from the University of Minnesota and his B.S. in Computer Engineering from Iowa State University. He has built a number of open-source software packages, open-source software distribution being a key part of his research. Recent examples include: LenKit - a toolkit for building, researching, and studying recommender systems, and Grapht – a dependency injection framework for Java with novel configuration and static analysis capabilities.

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About Michael D. Ekstrand

Starting at Boise State University in 2016, Dr. Michael D. Ekstrand specializes in researching human-computer interaction regarding recommender systems. Currently an assistant professor in the Department of Computer Science at Boise State University. Dr. Ekstrand received his Ph.D. in Computer Science from the University of Minnesota and his B.S. in Computer Engineering from Iowa State University. He has built a number of open-source software packages, open-source software distribution being a key part of his research. Recent examples include: LenKit - a toolkit for building, researching, and studying recommender systems, and Grapht – a dependency injection framework for Java with novel configuration and static analysis capabilities.

Articles (7)

Fairness in Information Access Systems

Foundations and Trends® in Information Retrieval (2022)

Michael D. Ekstrand, Anubrata Das, Robin Burke and Fernando Diaz

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts ...

The Multisided Complexity of Fairness in Recommender Systems

AI Magazine (2022)

Nasim Sonboli, Robin Burke, Michael Ekstrand and Rishabh Mehrotra

Recommender systems are poised at the interface between stakeholders: for example, job applicants and employers in the case of recommendations ...

Exploring Author Gender in Book Rating and Recommendation

User Modeling and User-Adapted Interaction (2021)

Michael D. Ekstrand and Daniel Kluver

Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good ...

Enhancing Classroom Instruction with Online News

Aslib Journal of Information Management (2020)

Michael D. Ekstrand, Katherine Landau Wright and Maria Soledad Pera

Purpose Investigate how school teachers look for informational texts for their classrooms. Access to current, varied, and authentic informational texts ...

Dependency Injection with Static Analysis and Context-Aware Policy

Journal of Object Technology (2016)

Michael D. Ekstrand and Michael Ludwig

The dependency injection design pattern improves the configurability, testability, and maintainability of object-oriented applications by decoupling components from both the ...

Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned ...

ACM Transactions on Computer-Human Interaction (TOCHI) (2015)

Joseph A. Konstan, J. D. Walker, D. Christopher Brooks, Michael D. Ekstrand, et al.

In the fall of 2013, we offered an open online Introduction to Recommender Systems through Coursera, while simultaneously offering a ...

Collaborative Filtering Recommender Systems

Foundations and Trends® in Human-Computer Ineraction (2011)

Michael D. Ekstrand, John T. Riedl and Joseph A. Konstan

Recommender systems are an important part of the information and e-commerce ecosystem. They represent a powerful method for enabling users ...

Contribution to Book

Rating-Based Collaborative Filtering: Algorithms and Evaluation

Social Information Access: Systems and Technologies (2018)

Daniel Kluver, Michael D. Ekstrand and Joseph A. Konstan

Recommender systems help users find information by recommending content that a user might not know about, but will hopefully like. ...

Contribution to Book

Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2022)

Amifa Raj and Michael D. Ekstrand

Information access systems, such as search and recommender systems, often use ranked lists to present results believed to be relevant ...

Contribution to Book

Estimation of Fair Ranking Metrics with Incomplete Judgments

WWW '21: Proceedings of the Web Conference 2021 (2021)

Ömer Kırnap, Fernando Diaz, Asia Biega, Michael Ekstrand, et al.

There is increasing attention to evaluating the fairness of search system ranking decisions. These metrics often consider the membership of ...

Contribution to Book

Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse

CEUR Workshop Proceedings (2021)

Ngozi Ihemelandu and Michael D. Ekstrand

This paper calls attention to the missing component of the recommender system evaluation process: Statistical Inference. There is active research ...

Contribution to Book

Evaluating Stochastic Rankings with Expected Exposure

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020)

Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, et al.

We introduce the concept of expected exposure as the average attention ranked items receive from users over repeated samples of ...

Contribution to Book

LensKit for Python: Next-Generation Software for Recommender Systems Experiments

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020)

Michael D. Ekstrand

LensKit is an open-source toolkit for building, researching, and learning about recommender systems. First released in 2010 as a Java ...

Estimating Error and Bias in Offline Evaluation Results

2020 Conference on Human Information Interaction and Retrieval (CHIIR ’20) (2020)

Mucun Tian and Michael D. Ekstrand

Offline evaluations of recommender systems attempt to estimate users’ satisfaction with recommendations using static data from prior user interactions. These ...

Contribution to Book

Fairness and Discrimination in Recommendation and Retrieval

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems (2019)

Michael D. Ekstrand, Robin Burke and Fernando Diaz

Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are ...

Contribution to Book

2nd FATREC Workshop: Responsible Recommendation

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems (2018)

Toshihiro Kamishima, Pierre-Nicolas Schwab and Michael D. Ekstrand

The second Workshop on Responsible Recommendation (FATREC 2018) was held in conjunction with the 12th ACM Conference on Recommender Systems ...

Exploring Author Gender in Book Rating and Recommendation

RecSys '18: Proceedigns of the 12th ACM Conference on Recommender Systems (2018)

Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, et al.

Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good ...

Monte Carlo Estimates of Evaluation Metric Error and Bias: Work ...

Computer Science Faculty Publications and Presentations (2018)

Mucun Tian and Michael D. Ekstrand

Traditional offline evaluations of recommender systems apply metrics from machine learning and information retrieval in settings where their underlying assumptions ...

Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and ...

Proceedings of ComplexRec 2018 Second Workshop on Recommendation in Complex Scenarios (2018)

Michael D. Ekstrand, Ion Madrazo Azpiazu, Katherine Landau Wright and Maria Soledad Pera

In this paper, we consider the promise and challenges of deploying recommendation and information retrieval technology to help teachers locate ...

The LKPY Package for Recommender Systems Experiments

Computer Science Faculty Publications and Presentations (2018)

Michael D. Ekstrand

Since 2010, we have built and maintained LensKit, an open-source toolkit for building, researching, and learning about recommender systems. We ...

Contribution to Book

FATREC Workshop on Responsible Recommendation Proceedings

Computer Science Faculty Publications and Presentations (2017)

Michael Ekstrand and Amit Sharma

We sought with this workshop, to foster a discussion of various topics that fall under the general umbrella of responsible ...

Contribution to Book

Recommender Response to Diversity and Popularity Bias in User Profiles

FLAIRS 2017: Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (2017)

Sushma Channamsetty and Michael D. Ekstrand

Recommender system evaluation usually focuses on the overall effectiveness of the algorithms, either in terms of measurable accuracy or ability ...

Contribution to Book

Sturgeon and the Cool Kids: Problems with Random Decoys for ...

FLAIRS 2017: Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (2017)

Michael D. Ekstrand and Vaibhav Mahant

Top-N evaluation of recommender systems, typically carried out using metrics from information retrieval or machine learning, has several challenges. Two ...

Contribution to Book

Behaviorism is Not Enough: Better Recommendations Through Listening to Users

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems (2016)

Michael D. Ekstrand and Martijn C. Willemsen

Behaviorism is the currently-dominant paradigm for building and evaluating recommender systems. Both the operation and the evaluation of recommender system ...

Letting Users Choose Recommender Algorithms: An Experimental Study

9th ACM Conference on Recommender Systems (2015)

Michael D. Ekstrand, Daniel Kluver, F. Maxwell Harper and Joseph A. Konstan