Marketplace Evaluation uses repeated-interaction simulations to assess information access systems with marketplace-level metrics such as retention and market share that complement traditional accuracy measures.
Gomez-Uribe and Neil Hunt
4 Pith papers cite this work. Polarity classification is still indexing.
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The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.
Autonomous agents sustain positive engagement lift in marketing personalization over time following initial human oversight in a real-world 11-month case study.
Controlled personalization combining editorial curation with modest algorithmic recommendations in legacy news increases engagement, diversity, and reduces popularity bias per an A/B test.
citing papers explorer
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Evaluation of Agents under Simulated AI Marketplace Dynamics
Marketplace Evaluation uses repeated-interaction simulations to assess information access systems with marketplace-level metrics such as retention and market share that complement traditional accuracy measures.
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Offline Evaluation Measures of Fairness in Recommender Systems
The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.
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Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study
Autonomous agents sustain positive engagement lift in marketing personalization over time following initial human oversight in a real-world 11-month case study.
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Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation
Controlled personalization combining editorial curation with modest algorithmic recommendations in legacy news increases engagement, diversity, and reduces popularity bias per an A/B test.