arXiv (All)
open access
EvoRank: LLM-Guided Evolution of Multi-Objective Learning-to-Rank Pipelines
artificial-intelligencecomputation-and-languageh-3-3i-2-6machine-learningneural-and-evolutionary-computing
We present EvoRank, an open autonomous ranking engineer: an LLM-guided evolutionary loop that discovers complete Learning-to-Rank pipelines (features, models, losses, ensembles) for multi-objective e-commerce search. On the Expedia ICDM 2013 dataset, with relevance, conversion, and revenue as compet
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
- OSCAR: Order-aware Scoring and Calibration for AI Rankings#1050313
- A crop recommendation using improved expert-guided constraint aware differential evolution and CatBoost surrogate learning.#430787
- UniRec: Cross-stage Multi-Task Fusion with Preference Alignment for Cascaded Recommender Systems#998615
- VerTox: Verifiable Reward-Guided Corpus Poisoning Against Neural Ranking Models#821272
- Assessing predictive performance of supervised machine learning algorithms: an alternative model for diamond pricing#956891
Record · ID 1036879
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