A new research paper addresses the critical training instability issues encountered in large-scale recommender systems, particularly within dynamic environments like short-form video platforms. The paper details a "G-W-L cascade"—gradient explosion, weight explosion, and loss explosion—that hinders model convergence. To combat this, the authors propose a structured mitigation framework targeting each stage of the cascade with specific control, stabilization, and shaping interventions. This framework is empirically validated on an industrial short-form video recommendation system, offering practical insights for deploying robust, continuously updated recommenders. AI
IMPACT Provides a structured framework for improving the stability and performance of large-scale recommender systems in dynamic environments.
RANK_REASON Research paper published on arXiv detailing a new framework for recommender systems. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.IR (Information Retrieval) →
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- failure
- Gotit.pub
- gradient
- gradient explosion
- G-W-L cascade
- Hugging Face
- large language model parameter
- loss explosion
- ScienceCast
- Short-Form Video App Dependency: The Role of Neuroticism, Lower Life Satisfaction, and Fear of Missing Out
- starting up
- weight explosion
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