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cs.IR2024★ 1 cited
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
Peilin Zhou, You-Liang Huang, Yueqi Xie +4
Sequential recommender systems (SRS) are designed to predict users' future behaviors based on their historical interaction data. Recent research has increasingly utilized contrasti…
cs.IR2022
Towards Reliable Item Sampling for Recommendation Evaluation
Dong Li, Ruoming Jin, Zhenming Liu +3
Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are "inconsistent" with respect to the global metrics (even in expectation), there have been a…