arXiv (All)
open access
Decision-Oriented Recommendation Reranking: An Empirical Study of Jev
Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation quality and serving efficiency. We investigate whether a decision-oriented model provides a useful alternative when the reranking task is fundamentally
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Evaluating Brand Retrieval and Ranking in Large Language Model Recommendations#1110670
- Which Reranking Conclusions Survive the Answer Interface? A Prospective Finite-Orbit Audit#1062150
- The Limits of Rank-Dependent Priorities in School Choice#1144392
- Efficient Offline Learning of Ranking Policies via Top-$k$ Policy Decomposition#1131941
- T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation#670348
Record · ID 1154504
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive