110 citations · 310 across the 10 of their papers we have counts for
5 papers · 1 filter
Knowledge-refined Denoising Network for Robust Recommendation
Xinjun Zhu, Yuntao Du, Yuren Mao +3
Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing know…
Towards Explainable Collaborative Filtering with Taste Clusters Learning
Yuntao Du, Jianxun Lian, Jing Yao +5
Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based…
Self-Guided Learning to Denoise for Robust Recommendation
Yunjun Gao, Yuntao Du, Yujia Hu +4
The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, w…
HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation
Yuntao Du, Xinjun Zhu, Lu Chen +2
Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability. A recent technical trend is to design end-to-end models ba…
MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation
Yuntao Du, Xinjun Zhu, Lu Chen +2
A knowledge graph (KG) consists of a set of interconnected typed entities and their attributes. Recently, KGs are popularly used as the auxiliary information to enable more accurat…