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cs.AI2026
On the Regularization Landscape for the Linear Recommendation Models
Dong Li, Zhenming Liu, Ruoming Jin +4
Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders on several standard recommendation benchmarks. Whil…
cs.AI2023
Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)
Dong Li, Ruoming Jin, Bin Ren
Inspired by the success of contrastive learning, we systematically examine recommendation losses, including listwise (softmax), pairwise (BPR), and pointwise (MSE and CCL) losses.…
cs.AI2023
(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)
Ruoming Jin, Dong Li
In this paper, we perform a systemic examination of the recommendation losses, including listwise (softmax), pairwise(BPR), and pointwise (mean-squared error, MSE, and Cosine Contr…