arXiv (All)
open access
From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation
Semantic IDs (SIDs) encode each catalog item as a short token sequence, enabling generative recommenders to predict the next item autoregressively. Reasoning-enhanced variants, an increasingly common extension, first generate a textual trace and then decode a next-item SID by beam search. Such recom
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
- Off-Policy Evaluation for Semantic ID Recommenders: Does the Model's Own Code Hierarchy Help?#793138
- Off-Policy Evaluation for Semantic ID Recommenders: Does the Model's Own Code Hierarchy Help?#789134
- Personalizing retrieval using joint embeddings; or "The Return of Fluffy"#219331
- ICEGR: An Intent-Coherent End-to-End Generative Retrieval Framework for E-commerce Search#998364
- SIREN (Luring LLMs onto the Rocks): PAIR-Driven Preference Manipulation in Web-RAG Recommenders#652403
Record · ID 1078054
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