Feipeng Guo

Intent-enhanced sequential recommendation via multi-view contrastive learning over stabilized essential intents

Sequential recommendation provides an effective way to model users' historical behaviors for predicting future interactions. However, the existing methods have not fully explored how to conduct stable intent data augmentation based on user sub-intents, and simultaneously construct multi-view self-supervised signals for...