440 citations · 942 across the 16 of their papers we have counts for
12 papers · 1 filter
VIP5: Towards Multimodal Foundation Models for Recommendation
Shijie Geng, Juntao Tan, Shuchang Liu +2
Computer Vision (CV), Natural Language Processing (NLP), and Recommender Systems (RecSys) are three prominent AI applications that have traditionally developed independently, resul…
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
Juntao Tan, Shijie Geng, Zuohui Fu +4
Structural data well exists in Web applications, such as social networks in social media, citation networks in academic websites, and threads data in online forums. Due to the comp…
User-oriented Fairness in Recommendation
Yunqi Li, Hanxiong Chen, Zuohui Fu +2
As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects…
Faithfully Explainable Recommendation via Neural Logic Reasoning
Yaxin Zhu, Yikun Xian, Zuohui Fu +2
Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation pr…
Discrete Knowledge Graph Embedding based on Discrete Optimization
Yunqi Li, Shuyuan Xu, Bo Liu +4
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable di…
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
Yikun Xian, Zuohui Fu, Handong Zhao +8
Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to gen…