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Found 4 papers, 4 papers with code
SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation
In the second stage, we devise a self-augmentation module to augment sequences to alleviate OUPs.
Adaptive Hardness Negative Sampling for Collaborative Filtering
Negative sampling is essential for implicit collaborative filtering to provide proper negative training signals so as to achieve desirable performance.
Augmented Negative Sampling for Collaborative Filtering
To balance efficiency and effectiveness, the vast majority of existing methods follow the two-pass approach, in which the first pass samples a fixed number of unobserved items by a simple static distribution and then the second pass selects the final negative items using a more sophisticated negative sampling strategy.
Denoising and Prompt-Tuning for Multi-Behavior Recommendation
In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e. g., click, add-to-cart, and purchase).
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