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Data Sparsity Issue
FICLRec: Frequency enhanced intent contrastive learning for sequential recommendation
Published:6/11/2025
Sequential Recommender SystemsFrequency Enhanced Intent Contrastive LearningUser Purchasing Behavior ModelingData Sparsity IssueReal-World Recommendation Datasets
FICLRec, a proposed model, uses frequencyenhanced intent contrastive learning to address the limitations of capturing highfrequency intents in sequential recommendation. It significantly improves performance across five realworld datasets.
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Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
Published:12/16/2021
Graph Contrastive Learning for RecommendationUser-Item Bipartite Graph AugmentationSelf-Supervised Signal ExtractionData Sparsity IssueEnhancement of Recommendation Accuracy
This study reveals that graph augmentations are unnecessary in contrastive learning for recommendations. The performance boost comes from the uniformity of representations. The proposed SimGCL uses uniform noise instead of complex augmentations, improving accuracy and efficiency.
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