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

END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation

no code implementations • 26 Mar 2024 • Yongqiang Han, Hao Wang, Kefan Wang, Likang Wu, Zhi Li, Wei Guo, Yong liu, Defu Lian, Enhong Chen

In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to a cart, and purchasing.

Denoising Sequential Recommendation +1

GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation

no code implementations • 1 Mar 2023 • Yongqiang Han, Likang Wu, Hao Wang, Guifeng Wang, Mengdi Zhang, Zhi Li, Defu Lian, Enhong Chen

Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item.

Contrastive Learning Sequential Recommendation