Contrastive Learning Method for Sequential Recommendation Based on Multi-Intention Disentanglement

The core message of this work is to propose a Contrastive Learning sequential recommendation method based on Multi-Intention Disentanglement (MIDCL) to effectively disentangle and leverage users' dynamic and diverse interactive intentions for improved sequential recommendation performance.