3 accepted papers
Low-Rank Adaptation (LoRA) has become the de facto paradigm for parameter-efficient fine-tuning, with its effectiveness critically influenced by rank allocation across modules. However, existing approaches face a fundamental dilemma: uniform allocation ignores module heterogeneity, while adaptive me…
Sequential recommendation aims to predict the next item a user is likely to interact with based on their historical interaction sequence. Capturing user intent is crucial in this process, as each interaction is typically driven by specific intentions (e.g., buying skincare products for skin maintena…
Recommendation techniques are important approaches for alleviating information overload. Being often trained on implicit user feedback, many recommenders suffer from the sparsity challenge due to the lack of explicitly negative samples. The GAN-style recommenders (i.e., IRGAN) addresses the challeng…