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Found 4 papers, 2 papers with code
AdaptiveRec: Adaptively Construct Pairs for Contrastive Learning in Sequential Recommendation
This paper presents a solution to the challenges faced by contrastive learning in sequential recommendation systems.
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation
Along with the exponential growth of online platforms and services, recommendation systems have become essential for identifying relevant items based on user preferences.
AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme
Mechanical defects in real situations affect observation values and cause abnormalities in multivariate time series, such as sensor values or network data.
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation
To alleviate this limitation, we propose a novel sequential recommendation framework based on gradient-based meta-learning that captures the imbalanced rating distribution of each user and computes adaptive loss for user-specific learning.
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