Search Results for author:
Found 6 papers, 5 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.
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).
