Dataset Regeneration for Sequential Recommendation

no code implementations • 28 May 2024 • Mingjia Yin, Hao Wang, Wei Guo, Yong liu, Suojuan Zhang, Sirui Zhao, Defu Lian, Enhong Chen

The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users.

Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation

no code implementations • 21 May 2024 • Mingjia Yin, Hao Wang, Wei Guo, Yong liu, Zhi Li, Sirui Zhao, Defu Lian, Enhong Chen

Cross-domain sequential recommendation (CDSR) aims to uncover and transfer users' sequential preferences across multiple recommendation domains.

Multi-Task Learning Self-Supervised Learning +1

A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation

no code implementations • 30 Mar 2024 • Luankang Zhang, Hao Wang, Suojuan Zhang, Mingjia Yin, Yongqiang Han, Jiaqing Zhang, Defu Lian, Enhong Chen

To this end, we propose a Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation (AREIL).

Disentanglement Multi-Task Learning

APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation

1 code implementation • 6 Nov 2023 • Mingjia Yin, Hao Wang, Xiang Xu, Likang Wu, Sirui Zhao, Wei Guo, Yong liu, Ruiming Tang, Defu Lian, Enhong Chen

To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems.

Graph Learning Multi-Task Learning +1