4 citations · 8 across the 8 of their papers we have counts for
8 papers
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
Yongjing Hao, Pengpeng Zhao, Junhua Fang +5
Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective a…
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation
Hanwen Du, Huanhuan Yuan, Pengpeng Zhao +4
Sequential recommendation aims to capture users' dynamic interest and predicts the next item of users' preference. Most sequential recommendation methods use a deep neural network…
Sequential Recommendation with Probabilistic Logical Reasoning
Huanhuan Yuan, Pengpeng Zhao, Xuefeng Xian +3
Deep learning and symbolic learning are two frequently employed methods in Sequential Recommendation (SR). Recent neural-symbolic SR models demonstrate their potential to enable SR…
Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation
Xinyu Du, Huanhuan Yuan, Pengpeng Zhao +5
Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data. Most existing methods model user preferenc…
Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes
Huixin Zhan, Victor S. Sheng
Although recent network representation learning (NRL) works in text-attributed networks demonstrated superior performance for various graph inference tasks, learning network repres…
Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract)
Huixin Zhan, Kun Zhang, Keyi Lu +1
In this paper, we measure the privacy leakage via studying whether graph representations can be inverted to recover the graph used to generate them via graph reconstruction attack…