arXiv (All)
open access
Recommender System as Slow and Fast Thinkers
Sequential recommendation models are foundational to modern personalized services, yet their effectiveness varies substantially across heterogeneous user environments. In particular, static one-pass recommenders often perform well on common behavior patterns but degrade on operationally challenging
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
- Recommender System as Slow and Fast Thinkers#920581
- Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanism#1011978
- Training seeds and model-selection stability in recommender-system evaluation#924376
- Training seeds and model-selection stability in recommender-system evaluation#920145
- Two-Sided State-Space Models for Sequential Recommendation with Non-Random Multimodal Review Feedback#819633
Record · ID 924815
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