arXiv (OAI Expanded)
open access
SORT: A Systematically Optimized Ranking Transformer for Industrial-scale Recommenders
While Transformers have achieved remarkable success in LLMs through superior scalability, their application in industrial-scale ranking models remains nascent, hindered by the challenges of high feature sparsity and low label density. In this paper, we propose SORT (Systematically Optimized Ranking
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
- SORT: A Systematically Optimized Ranking Transformer for Industrial-scale Recommenders#798788
- SumRank: Aligning Summarization Models for Long-Document Listwise Reranking#998158
- LazFormer: Scaling Transformers for Industrial Recommendation via Transferable Generative Pre-training#1084880
- An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking#1011043
- UniRec: Cross-stage Multi-Task Fusion with Preference Alignment for Cascaded Recommender Systems#998615
Record · ID 797374
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