Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation
Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation
Self-supervised sequential recommendation significantly improves recommendation performance by maximizing mutual information with well-designed data augmentations. However, the mutual information estimation is based on the calculation of Kullback–Leibler divergence with several limitations, including asymmetrical estimation, the exponential need of the sample size, and training instability. Also, existing data augmentations are mostly …