arXiv (All)
open access
AutoLR: Automating the Path from Research to Launch Review in Industrial Recommender Systems
Improving an industrial recommender is an iterative research-and-engineering process rather than a direct path from idea to deployment. In \textbf{DASHEN, NetEase's gaming-community app}, algorithm engineers typically identify promising directions from research papers, technical reports, and prior p
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
- AutoRecLab: Describe the Experiment, Get the Code!#1036252
- AutoRecLab: Describe the Experiment, Get the Code!#1006858
- SetMIR: Multi-Interest Retrieval as Set Prediction#799467
- SetMIR: Multi-Interest Retrieval as Set Prediction#798044
- RecEvolve: A Knowledge-Driven Autonomous Agent System for Recommender Systems#822062
Record · ID 971532
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