arXiv (All)
open access
UniRec: Cross-stage Multi-Task Fusion with Preference Alignment for Cascaded Recommender Systems
Industrial recommender systems use cascaded stages with different objectives, feature spaces, and latency constraints. Optimizing pre-ranking and ranking separately can create cross-stage inconsistency: upstream models may filter out items preferred by downstream rankers, and independently tuned dow
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
- Task-Blind No MORE: Multi-Task Information Flow in Unified Ranking Backbones#975324
- Lightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems#1061073
- GenFacet: End-to-End Generative Faceted Search via Multi-Task Preference Alignment in E-Commerce#652235
- GenFacet: End-to-End Generative Faceted Search via Multi-Task Preference Alignment in E-Commerce#650782
- IntHQ: Task-Interactive Hierarchical Query on Dual-Stream Representations for Generative Recommendation#1035585
Record · ID 998615
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