A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
arXiv 2605.16344•2499a02752adb134d5adcc04bd4b890e6f181bcc60f328952b16eb53227a844c
LLM-adsad-auctionsbenchmarksbioinformaticscode-retrievaldeploymentevaluationfairnessindustrial-mljob-searchmulti-label-classificationpareto-frontierpersonalizationpopularity-biasproduction-systemsprotein-qarecommender-systemsreinforcement-learningretrieval-augmented-generationsoftware-engineering
Paper metadata
- arXiv ID
- 2605.16344
- Version
- Not specified by this published record
- Category
- Computer Science — Information Retrieval (cs.IR)
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Evidence and limitations
- Source ID
- arxiv_cs_ir
- Record identifier
- 2499a02752adb134d5adcc04bd4b890e6f181bcc60f328952b16eb53227a844c
- Enrichment time
- 2026-05-19T08:52:15Z
- AI-assisted enrichment
- Yes
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