arXiv Machine Learning
Jun 26
Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking
arXiv:2606. 26369v1 Announce Type: cross Abstract: Scoring functions are used to represent the relevance of individual documents.
Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratification
arXiv:2606. 04110v1 Announce Type: new Abstract: Online evaluation of ranking and retrieval systems often relies on downstream monetization metrics such as app revenue or creator earnings.
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arXiv AI
Jun 10
Quantifying Uncertainty in AI Visibility: A Statistical Framework for Generative Search Measurement
arXiv:2603. 08924v2 Announce Type: replace-cross Abstract: AI-powered answer engines are inherently non-deterministic: identical queries submitted at different times can produce different responses and cite different sources.
arXiv Machine Learning
Aug 13
Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes
arXiv:2608. 11390v1 Announce Type: new Abstract: Generative engines are reshaping the web ecosystem by making citations a key mechanism for allocating attention, attribution, and downstream value.
arXiv Machine Learning
Jun 16
Contextual Bandits for Maximizing Stimulated Word-of-Mouth Rewards
arXiv:2606. 15146v1 Announce Type: new Abstract: Stimulated word-of-mouth is a strategy that promotes information sharing through prompts or incentives.
arXiv Machine Learning
Jul 10
Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
arXiv:2508. 11847v4 Announce Type: replace-cross Abstract: We propose a method for evaluating the robustness of widely used LLM ranking systems -- variants of a Bradley--Terry model -- to dropping a worst-case very small fraction of preference data.
arXiv Machine Learning
Jul 17
Adaptive Ad Load Design for Sponsored Search Markets: Evidence, Theory, and Deployment
arXiv:2607. 14418v1 Announce Type: new Abstract: Ad-load design is a central supply-side decision in sponsored search: more sponsored slots can raise revenue, but may crowd out organic results and degrade user outcomes.
arXiv AI
Jun 4
Knowledge Index of Noah's Ark
arXiv:2606. 05104v1 Announce Type: new Abstract: Knowledge benchmarks for LLMs face three issues: scaling-driven designs that do not operationalize disciplinary representativeness; flat-payment annotation that permits lazy consensus; and unaudited ranking instability under bounded test budgets.
arXiv Machine Learning
Jun 29
Learning Peer Influence Probabilities with Linear Contextual Bandits
arXiv:2510. 19119v2 Announce Type: replace Abstract: In networked environments, it is common for users to share recommendations about content, products, services, and possible courses of action.
arXiv AI
Jun 12
One Polluted Page Is Enough: Evaluating Web Content Pollution in Generative Recommenders
arXiv:2606. 13610v1 Announce Type: cross Abstract: Search-augmented LLMs increasingly mediate everyday consumer recommendations by retrieving live web content.
arXiv Machine Learning
Jul 27
PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest
arXiv:2607. 22518v1 Announce Type: cross Abstract: In this paper, we propose a new solution for addressing the content cold-start problem in industry-scale search and recommender systems.
arXiv Machine Learning
Jun 8
Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
arXiv:2606. 07492v1 Announce Type: cross Abstract: The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale.
arXiv AI
2d ago
Dear Algo: A Precision-First Agentic Intent Layer for Unified Search and Recommendation
arXiv:2608. 15877v1 Announce Type: new Abstract: Search and recommendation serve a shared discovery objective but encode intent differently.