Faculty

Institution

Unit

Venue

Michael Ekstrand

Assistant Professor

Drexel University

Affiliations

College of Computing & Informatics

Department of Information Science

Michael Ekstrand is currently serving as a College Of Computing & Informatics Assistant Professor at Drexel University.

Honors

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Experiences

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Education

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Michael Ekstrand

Drexel University

Assistant Professor

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computer science machine learning recommender systems fairness information retrieval data science behavioral sciences systems engineering human-computer interaction research ethics data mining social media user modeling responsible recommendation performance evaluation teaching methods collaborative filtering ranking algorithms evidence-based recommendations popular culture information technology decision science distance education knowledge discovery retrieval technique reputation management accountability conversational recommender system online education information access systems recommender systems experiments fatrec workshop recommender systems notation reading research information management harm reduction animal behavior fish biology avian biology textual practice screen medium educational psychology knowledge management user perception automated knowledge acquisition audience reception societal influence failover failure analysis group recommenders failure detection human error privacy protection data retrieval social ranking synthetic data performance prediction performance modeling computer-based education curriculum & instruction information education educational communication coral reefs benchmark studies gender theory stochastic rankings recommender research ecosystem recommender algorithms rating-based collaborative filtering research reading lists interdisciplinary synthesis demographic biases recommender evaluation next-generation software recommender accuracy application context technology algorithmic bias ethics of artificial intelligence ai adoption social impact algorithmic governmentality intelligent computing artificial intelligence responsible ai ai ethics

Notable Scholarly Works

Notable Federal Grants

Collaborators

ALTRECSYS: A Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation. (2024)

Michael EkstrandMaria Soledad PeraAlan Said

critical ideasmachine learningrecommender systemsdata sciencebehavioral sciencesevidence-based recommendations

Conducting Recommender Systems User Studies Using Poprox. (2024)

Robin BurkeJoseph KonstanMichael Ekstrand

text mininghuman-computer interactionuser analysisonline informationsocial mediadata miningsemantic evaluationcollaborative filteringcomputer sciencerecommender systemsdata scienceanalyticsuser profilinguser researchinformation retrievaluser-centric evaluationknowledge discovery

Facctrec 2024: The 7Th Workshop on Responsible Recommendation. (2024)

Michael D. EkstrandToshihiro KamishimaAmifa RajKarlijn Dinnissen

responsible recommendation

It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation. (2024)

Andres FerraroMichael D. EkstrandChristine Bauer

music recommendationchoice modelsmusicgender imbalancegender inequality

Multiple Testing for Ir and Recommendation System Experiments. Lecture notes in computer science (2024)

Ngozi IhemelanduMichael D. Ekstrand

software testingrecommendation system experimentscomputer sciencemachine learningrecommender systems

Not Just Algorithms: Strategically Addressing Consumer Impacts in Information Retrieval. Lecture notes in computer science (2024)

Michael D. EkstrandLex BeattieMaria Soledad PeraHenriette Cramer

information retrievalstrategic studiescomputer scienceconsumer impacts

Towards Optimizing Ranking in Grid-Layout for Provider-Side Fairness. Lecture notes in computer science (2024)

Amifa RajMichael D. Ekstrand

provider-side fairnessreputation systemsanalyticscomputer science

Building Human Values Into Recommender Systems: An Interdisciplinary Synthesis. ACM Transactions on Recommender Systems (2023)

Jonathan StrayAlon HalevyParisa AssarDylan Hadfield-MenellCraig BoutilierAmar AsharChloe BakalarLex BeattieMichael EkstrandClaire LeibowiczConnie Moon SehatSara JohansenLianne KerlinDavid VickreySpandana SinghSanne VrijenhoekAmy ZhangMcKane AndrusNatali HelbergerPolina ProutskovaTanushree MitraNina Vasan

interdisciplinary synthesissystems engineering

Candidate Set Sampling for Evaluating Top-N Recommendation. (2023)

Ngozi IhemelanduMichael D. Ekstrand

top-n recommendationmachine learningsamplingstatistics

Distributionally-Informed Recommender System Evaluation. ACM Transactions on Recommender Systems (2023)

Michael D. EkstrandBen CarteretteFernando Diaz

systems engineering

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Michael Ekstrand

Assistant Professor

Drexel University

Affiliations

College of Computing & Informatics

Department of Information Science

Michael Ekstrand is currently serving as a College Of Computing & Informatics Assistant Professor at Drexel University.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Michael Ekstrand

Drexel University

Assistant Professor

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

ALTRECSYS: A Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation. (2024)

Michael EkstrandMaria Soledad PeraAlan Said

critical ideasmachine learningrecommender systemsdata sciencebehavioral sciencesevidence-based recommendations

Conducting Recommender Systems User Studies Using Poprox. (2024)

Robin BurkeJoseph KonstanMichael Ekstrand

Facctrec 2024: The 7Th Workshop on Responsible Recommendation. (2024)

Michael D. EkstrandToshihiro KamishimaAmifa RajKarlijn Dinnissen

responsible recommendation

It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation. (2024)

Andres FerraroMichael D. EkstrandChristine Bauer

music recommendationchoice modelsmusicgender imbalancegender inequality

Multiple Testing for Ir and Recommendation System Experiments. Lecture notes in computer science (2024)

Ngozi IhemelanduMichael D. Ekstrand

software testingrecommendation system experimentscomputer sciencemachine learningrecommender systems

Not Just Algorithms: Strategically Addressing Consumer Impacts in Information Retrieval. Lecture notes in computer science (2024)

Michael D. EkstrandLex BeattieMaria Soledad PeraHenriette Cramer

information retrievalstrategic studiescomputer scienceconsumer impacts

Towards Optimizing Ranking in Grid-Layout for Provider-Side Fairness. Lecture notes in computer science (2024)

Amifa RajMichael D. Ekstrand

provider-side fairnessreputation systemsanalyticscomputer science

Building Human Values Into Recommender Systems: An Interdisciplinary Synthesis. ACM Transactions on Recommender Systems (2023)

interdisciplinary synthesissystems engineering

Candidate Set Sampling for Evaluating Top-N Recommendation. (2023)

Ngozi IhemelanduMichael D. Ekstrand

top-n recommendationmachine learningsamplingstatistics

Distributionally-Informed Recommender System Evaluation. ACM Transactions on Recommender Systems (2023)

Michael D. EkstrandBen CarteretteFernando Diaz

systems engineering

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