Making Information Systems Good for People
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Chained to the Rhythm
Learning Analogies
Analogies Run Amok
What We Do
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How Do They Work?
Recommender Vocabulary
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Recommender Architecture
User-Based Recommendations
Item-Based Recommendations
Matrix Factorization
Other Techniques
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How Do We Know It Worked?
Offline evaluation
Online evaluation (A/B testing)
Lab-style user studies
Experimental Protocol
buildingresearching learning about
LensKit in Use
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When Recommenders FailEkstrand and Riedl, RecSys 2012
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User-Perceived DifferencesEkstrand et al., RecSys 2014
Experiment Features
Results in Differences
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Problems with EvaluationEkstrand and Mahant, FLAIRS 2017
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Misclassified Decoys
Sturgeon’s Decoys
Who Benefits from Recommendations?
Fairness in Recommendation and Search
Consumers Producers
Groups
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🧛👸🧙Individuals
Differences Exist
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Reciprocity [Franklin, 1989]
Giving Users a Voice
Sample of Ethical Issues
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ACM Code of Ethics
Propagating Bias?(Under Review)
Feedback Loops(Future Work)
Promote Misinformation
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Promote Clickbait
Limits of Behavioral Observation
Information Disclosure
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In Search of James Comey
Fair Privacy(w/ Hoda Mehrpouyan, FAT* 2018)
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Beyond Recommenders
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The Real World of Technology
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Paths Forward
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