BIPCL improves sequential recommendation accuracy by bilaterally injecting collective intent prototypes into representations and enforcing contrastive alignment via bounded embedding perturbations.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Introduces the LLM ORDER BY semantic operator with algorithmic improvements, a semantic-aware external merge sort, and a budget-aware optimizer that selects near-optimal access paths for LLM-based ordering.
citing papers explorer
-
BIPCL: Bilateral Intent-Enhanced Sequential Recommendation via Embedding Perturbation Contrastive Learning
BIPCL improves sequential recommendation accuracy by bilaterally injecting collective intent prototypes into representations and enforcing contrastive alignment via bounded embedding perturbations.
-
Access Paths for Efficient Ordering with Large Language Models
Introduces the LLM ORDER BY semantic operator with algorithmic improvements, a semantic-aware external merge sort, and a budget-aware optimizer that selects near-optimal access paths for LLM-based ordering.