SPRINT refines LLM-generated intents for session-based recommendation via a global intent pool, performance validation, selective LLM invocation during training, and a lightweight intent predictor for scalable inference without LLM calls.
Advances in Neural Information Processing Systems 36 (2023), 46534–46594
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2025 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.
citing papers explorer
-
SPRINT: Scalable and Predictive Intent Refinement for LLM-Enhanced Session-based Recommendation
SPRINT refines LLM-generated intents for session-based recommendation via a global intent pool, performance validation, selective LLM invocation during training, and a lightweight intent predictor for scalable inference without LLM calls.
-
A Survey of Scaling in Large Language Model Reasoning
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.