Making Information Systems Good for People
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User-Based Recommendations
Item-Based Recommendations
How Do We Know It Worked?
Offline evaluation
Online evaluation (A/B testing)
Lab-style user studies
buildingresearching learning about
When Recommenders FailEkstrand and Riedl, RecSys 2012
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User-Perceived DifferencesEkstrand et al., RecSys 2014
Problems with EvaluationEkstrand and Mahant, FLAIRS 2017
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Who Benefits from Recommendations?
Fairness in Recommendation and Search
Consumers Producers
Groups
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🧛👸🧙Individuals
Reciprocity [Franklin, 1989]
Propagating Bias?(Under Review)
Feedback Loops(Future Work)
Limits of Behavioral Observation
Fair Privacy(w/ Hoda Mehrpouyan, FAT* 2018)
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The Real World of Technology
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