79 citations · 115 across the 9 of their papers we have counts for
8 papers
Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation
Qiang Liu, Zhaocheng Liu, Zhenxi Zhu +2
Recently, multi-interest models, which extract interests of a user as multiple representation vectors, have shown promising performances for sequential recommendation. However, non…
Metamobility: Connecting Future Mobility with Metaverse
Haoxin Wang, Ziran Wang, Dawei Chen +3
A Metaverse is a perpetual, immersive, and shared digital universe that is linked to but beyond the physical reality, and this emerging technology is attracting enormous attention…
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
Mao Ye, Ruichen Jiang, Haoxiang Wang +6
One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and…
Let us Build Bridges: Understanding and Extending Diffusion Generative Models
Xingchao Liu, Lemeng Wu, Mao Ye +1
Diffusion-based generative models have achieved promising results recently, but raise an array of open questions in terms of conceptual understanding, theoretical analysis, algorit…
Improving Multi-Interest Network with Stable Learning
Zhaocheng Liu, Yingtao Luo, Di Zeng +4
Modeling users' dynamic preferences from historical behaviors lies at the core of modern recommender systems. Due to the diverse nature of user interests, recent advances propose t…
Network Pruning via Feature Shift Minimization
Yuanzhi Duan, Yue Zhou, Peng He +3
Channel pruning is widely used to reduce the complexity of deep network models. Recent pruning methods usually identify which parts of the network to discard by proposing a channel…