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Found 5 papers, 2 papers with code
SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
Motivated by this insight, we empower small language models for SR, namely SLMRec, which adopt a simple yet effective knowledge distillation method.
Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values and those missing values are generally missing-not-at-random, which deteriorates the prediction performance of models.
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems.
Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations
The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks.
An Adaptive Oversampling Learning Method for Class-Imbalanced Fault Diagnostics and Prognostics
Data-driven fault diagnostics and prognostics suffers from class-imbalance problem in industrial systems and it raises challenges to common machine learning algorithms as it becomes difficult to learn the features of the minority class samples.
