most citedImproving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

557 citations · 569 across the 7 of their papers we have counts for

collaborators

8 papers

cs.IR20226 cited

Recent Advances in RecBole: Extensions with more Practical Considerations

Lanling Xu, Zhen Tian, Gaowei Zhang +10

RecBole has recently attracted increasing attention from the research community. As the increase of the number of users, we have received a number of suggestions and update request…

cs.LG2022

Privacy-Preserved Neural Graph Similarity Learning

Yupeng Hou, Wayne Xin Zhao, Yaliang Li +1

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are fre…

cs.IR20221 cited

CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space

Yupeng Hou, Binbin Hu, Zhiqiang Zhang +1

Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session. However, session embedding learned…

cs.IR2022

Leveraging Search History for Improving Person-Job Fit

Yupeng Hou, Xingyu Pan, Wayne Xin Zhao +4

As the core technique of online recruitment platforms, person-job fit can improve hiring efficiency by accurately matching job positions with qualified candidates. However, existin…

cs.LG2022

Neural Graph Matching for Pre-training Graph Neural Networks

Yupeng Hou, Binbin Hu, Wayne Xin Zhao +3

Recently, graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. However, when adapted to downstream tasks, it usually requires abundant task-s…

cs.IR2022557 cited

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

Zihan Lin, Changxin Tian, Yupeng Hou +1

Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users' preference over items by modeling the user-item…