Michael D. Ekstrand

Publications87
h-index20
Citations2,227
Highly Influential Citations164
Author pages are created from data sourced from our academic publisher partnerships and public sources.

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  • Publications
  • Influence
Collaborative Filtering Recommender Systems
TLDR
A wide variety of the choices available and their implications are discussed, aiming to provide both practicioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.
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Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
TLDR
The utility of LensKit is demonstrated by replicating and extending a set of prior comparative studies of recommender algorithms, and a question recently raised by a leader in the recommender systems community on problems with error-based prediction evaluation is investigated.
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Evaluating Stochastic Rankings with Expected Exposure
TLDR
A general evaluation methodology based on expected exposure is proposed, allowing a system, in response to a query, to produce a distribution over rankings instead of a single fixed ranking.
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Automatically building research reading lists
TLDR
This work explores several methods for augmenting existing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of citations, including a novel method for using importance scores to influence collaborative filtering.
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User perception of differences in recommender algorithms
TLDR
It is found that satisfaction is negatively dependent on novelty and positively dependent on diversity in this setting, and that satisfaction predicts the user's final selection of a recommender that they would like to use in the future.
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Rating-Based Collaborative Filtering: Algorithms and Evaluation
TLDR
The concepts, algorithms, and means of evaluation that are at the core of collaborative filtering research and practice are reviewed, and two more recent directions in recommendation algorithms are presented: learning-to-rank and ensemble recommendation algorithms.
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Behaviorism is Not Enough: Better Recommendations through Listening to Users
TLDR
It is argued that listening to what users say about the items and recommendations they like, the control they wish to exert on the output, and the ways in which they perceive the system will enable important developments in the future of recommender systems.
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All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness
TLDR
System evaluation protocols that explicitly quantify the degree to which the system is meeting the information needs of all its users are proposed, as well as the need for researchers and operators to move beyond evaluations that favor the needs of larger subsets of the user population while ignoring smaller subsets.
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When recommenders fail: predicting recommender failure for algorithm selection and combination
TLDR
This work presents an analysis of the predictions made by several well-known recommender algorithms on the MovieLens 10M data set, showing that for many cases in which one algorithm fails, there is another that will correctly predict the rating.
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Exploring author gender in book rating and recommendation
TLDR
This work measures the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data to find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution.
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