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Found 4 papers, 3 papers with code
Toward a Better Understanding of Loss Functions for Collaborative Filtering
Inspired by this analysis, we propose a novel loss function that improves the design of alignment and uniformity considering the unique patterns of datasets called Margin-aware Alignment and Weighted Uniformity (MAWU).
uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering
In this paper, we propose Unbiased ConTrastive Representation Learning (uCTRL), optimizing alignment and uniformity functions derived from the InfoNCE loss function for CF models.
Bilateral Self-unbiased Learning from Biased Implicit Feedback
Implicit feedback has been widely used to build commercial recommender systems.
Collaborative Distillation for Top-N Recommendation
Knowledge distillation (KD) is a well-known method to reduce inference latency by compressing a cumbersome teacher model to a small student model.
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