LLM-built attribute graphs enable zero-shot entity ranking in e-commerce with over 5% average precision gains and 57% less token usage per product compared to raw-text baselines.
ISBN 9798400701030
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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.
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From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Ranking
LLM-built attribute graphs enable zero-shot entity ranking in e-commerce with over 5% average precision gains and 57% less token usage per product compared to raw-text baselines.
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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.