arXiv (All)
open access
SelfDR: Self-Distillation from Reasoning for LLM-Based Recommendation
Large Language Models (LLMs) have recently emerged as powerful backbones for recommendation. To better elicit their capabilities, reasoning has been widely incorporated to help LLMs interpret rich textual signals and improve recommendation accuracy. However, explicitly generating intermediate reason
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
- Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanism#1011978
- From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning#282788
- Reasoning Quality Matters: Combating Reasoning Collapse in LLM-based Embedding Learning#1034376
- Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation#1129178
- Revisiting Complete Reasoning Traces for Post-Training#975163
Record · ID 925656
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