17 citations · 25 across the 3 of their papers we have counts for
3 papers
cs.IR2021★ 17 cited
Learning Explicit User Interest Boundary for Recommendation
Jianhuan Zhuo, Qiannan Zhu, Yinliang Yue +1
The core objective of modelling recommender systems from implicit feedback is to maximize the positive sample score and minimize the negative sample score , which can us…
cs.CL2021★ 1 cited
Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding
Ren Li, Yanan Cao, Qiannan Zhu +2
Modeling of relation pattern is the core focus of previous Knowledge Graph Embedding works, which represents how one entity is related to another semantically by some explicit rela…
cs.CL2021★ 7 cited
How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View
Ren Li, Yanan Cao, Qiannan Zhu +4
Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specific…