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Found 4 papers, 3 papers with code
Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data
Implementing the PNN paradigm is, however, technically challenging because: (1) it is difficult to classify unlabeled data into neutral or negative in the absence of supervised signals; (2) there does not exist any loss function that can handle set-level triple-wise ranking relationships.
From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking
However, bridging the gap in practice encounters two formidable challenges: (1) none of the real-world datasets contains full ranking information; (2) there does not exist a loss function that is capable of consuming ranking information.
SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation
In the second stage, we devise a self-augmentation module to augment sequences to alleviate OUPs.
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.
