Search Results for author: Michael D. Ekstrand

Found 13 papers, 4 papers with code

Multiversal Simulacra: Understanding Hypotheticals and Possible Worlds Through Simulation

no code implementations2 Oct 2021 Michael D. Ekstrand

Recommender systems research is concerned with many aspects of recommender system behavior and effects than simply its effectiveness, and simulation can be a powerful tool for uncovering these effects.

Recommendation Systems

Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse

no code implementations14 Sep 2021 Ngozi Ihemelandu, Michael D. Ekstrand

In this paper, we argue that the use of statistical inference is a key component of the evaluation process that has not been given sufficient attention.

Information Retrieval Recommendation Systems

Pink for Princesses, Blue for Superheroes: The Need to Examine Gender Stereotypes in Kid's Products in Search and Recommendations

no code implementations13 May 2021 Amifa Raj, Ashlee Milton, Michael D. Ekstrand

In this position paper, we argue for the need to investigate if and how gender stereotypes manifest in search and recommender systems. As a starting point, we particularly focus on how these systems may propagate and reinforce gender stereotypes through their results in learning environments, a context where teachers and children in their formative stage regularly interact with these systems.

Fairness and Discrimination in Information Access Systems

1 code implementation12 May 2021 Michael D. Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems.

Fairness Information Retrieval

Evaluating Stochastic Rankings with Expected Exposure

no code implementations27 Apr 2020 Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette

We introduce the concept of \emph{expected exposure} as the average attention ranked items receive from users over repeated samples of the same query.

Information Retrieval

Overview of the TREC 2019 Fair Ranking Track

no code implementations25 Mar 2020 Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, Sebastian Kohlmeier

The goal of the TREC Fair Ranking track was to develop a benchmark for evaluating retrieval systems in terms of fairness to different content providers in addition to classic notions of relevance.


Estimating Error and Bias in Offline Evaluation Results

1 code implementation26 Jan 2020 Mucun Tian, Michael D. Ekstrand

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

Recommendation Systems

Proceedings of FACTS-IR 2019

no code implementations12 Jul 2019 Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D. Ekstrand

The proceedings list for the program of FACTS-IR 2019, the Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval held at SIGIR 2019.

Fairness Information Retrieval

Recommender Systems Notation: Proposed Common Notation for Teaching and Research

no code implementations4 Feb 2019 Michael D. Ekstrand, Joseph A. Konstan

In the course of years of teaching and research on recommender systems, we have seen the val-ue in adopting a consistent notation across our work.

Information Retrieval Recommendation Systems

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

2 code implementations10 Sep 2018 Michael D. Ekstrand

LensKit is an open-source toolkit for building, researching, and learning about recommender systems.

Collaborative Filtering Recommendation Systems

Exploring Author Gender in Book Rating and Recommendation

2 code implementations22 Aug 2018 Michael D. Ekstrand, Daniel Kluver

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

Collaborative Filtering

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