10 papers
Enhancing Protein Representation Learning via Manifold Restore Mixing
Yizhou Dang, Chuang Zhao, Lianbo Ma +3
Data augmentation (DA) has been proven to be an effective means for improving protein representation learning (PRL) by generating additional training samples. Although mainstream p…
Pay Attention to Sequence Split: Uncovering the Impacts of Sub-Sequence Splitting on Sequential Recommendation Models
Yizhou Dang, Yifan Wu, Minhan Huang +5
Sub-sequence splitting (SSS) has been demonstrated as an effective approach to mitigate data sparsity in sequential recommendation (SR) by splitting a raw user interaction sequence…
Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation
Zhifu Wei, Yizhou Dang, Guibing Guo +2
Sequential Recommendation (SR) learns user preferences from their historical interaction sequences and provides personalized suggestions. In real-world scenarios, most items exhibi…
Tail-Aware Data Augmentation for Long-Tail Sequential Recommendation
Yizhou Dang, Zhifu Wei, Minhan Huang +4
Sequential recommendation (SR) learns user preferences based on their historical interaction sequences and provides personalized suggestions. In real-world scenarios, most users ca…
Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Chu Zhao, Eneng Yang, Yizhou Dang +3
Heuristic negative sampling enhances recommendation performance by selecting negative samples of varying hardness levels from predefined candidate pools to guide the model toward l…
Repeated Padding+: Simple yet Effective Data Augmentation Plugin for Sequential Recommendation
Yizhou Dang, Yuting Liu, Enneng Yang +4
Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted t…