arXiv (All)
open access
Inherit4Rec: Parameter Inheritance for Efficient Scaling of Recommendation Models
Scaling model capacity has emerged as an effective approach to overcoming performance bottlenecks in industrial recommender systems. However, repeatedly training larger dense models from scratch demands substantial data and time, while their growing computation conflicts with the strict serving budg
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
- Optimizing Effective Training Time for Large-Scale Recommendation Systems#1157405
- UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation#625350
- UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation#623077
- UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation#459697
- SequenceO1: End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Caching#985796
Record · ID 1048858
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