5 citations · 9 across the 6 of their papers we have counts for
7 papers
RAGAR: Retrieval Augmented Personalized Image Generation Guided by Recommendation
Run Ling, Wenji Wang, Yuting Liu +12
Personalized image generation is crucial for improving the user experience, as it renders reference images into preferred ones according to user visual preferences. Although effect…
Data Augmentation for Sequential Recommendation: A Survey
Yizhou Dang, Enneng Yang, Yuting Liu +4
As an essential branch of recommender systems, sequential recommendation (SR) has received much attention due to its well-consistency with real-world situations. However, the wides…
Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion
Pengxiang Lan, Enneng Yang, Yuting Liu +3
Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, w…
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…
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation
Yuting Liu, Enneng Yang, Yizhou Dang +5
Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations. Existing research ha…
Video and Audio are Images: A Cross-Modal Mixer for Original Data on Video-Audio Retrieval
Zichen Yuan, Qi Shen, Bingyi Zheng +3
Cross-modal retrieval has become popular in recent years, particularly with the rise of multimedia. Generally, the information from each modality exhibits distinct representations…
Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation
Yizhou Dang, Enneng Yang, Guibing Guo +5
Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transiti…