1 citations · 1 across the 5 of their papers we have counts for
5 papers
Rethinking Purity and Diversity in Multi-Behavior Sequential Recommendation from the Frequency Perspective
Yongqiang Han, Kai Cheng, Kefan Wang +1
In recommendation systems, users often exhibit multiple behaviors, such as browsing, clicking, and purchasing. Multi-behavior sequential recommendation (MBSR) aims to consider thes…
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
Hao Wang, Yongqiang Han, Kefan Wang +6
In the realm of recommendation systems, users exhibit a diverse array of behaviors when interacting with items. This phenomenon has spurred research into learning the implicit sema…
A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation
Luankang Zhang, Hao Wang, Suojuan Zhang +5
Cross-domain recommendation (CDR), aiming to extract and transfer knowledge across domains, has attracted wide attention for its efficacy in addressing data sparsity and cold-start…
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
Yongqiang Han, Hao Wang, Kefan Wang +6
In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to a cart, and purchasing. However, with diversified behavior data, user…
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation
Yongqiang Han, Likang Wu, Hao Wang +5
Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item. Although lots of r…