3 accepted papers
Traditional recommenders often fail to disentangle the motivations behind user choices. To address this, we propose MV-LLMRec, a framework that models interactions through three views: Structural, Intent, and Conformity. MV-LLMRec leverages LLMs to generate rich semantic representations for intent a
Uncertainty quantification (UQ) in graph neural networks (GNNs) is crucial in high-stakes domains but remains a significant challenge. In graph settings, message passing often relies on strong assumptions such as exchangeability, which are rarely satisfied in practice. Moreover, achieving reliable U…
Many policy gradient methods are variants of Actor-Critic (AC), where a value function (critic) is learned to facilitate updating the parameterized policy (actor). The update to the actor involves a log-likelihood update weighted by the action-values, with the addition of entropy regularization for…