10 papers
Federated Learning and Unlearning for Recommendation with Personalized Data Sharing
Liang Qu, Jianxin Li, Wei Yuan +4
Federated recommender systems (FedRS) have emerged as a paradigm for protecting user privacy by keeping interaction data on local devices while coordinating model training through…
Scalable Dynamic Embedding Size Search for Streaming Recommendation
Yunke Qu, Liang Qu, Tong Chen +3
Recommender systems typically represent users and items by learning their embeddings, which are usually set to uniform dimensions and dominate the model parameters. However, real-w…
Towards On-Device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model
Zhaofeng Zhong, Wei Yuan, Liang Qu +4
With the advancement of large language models (LLMs), significant progress has been achieved in various Natural Language Processing (NLP) tasks. However, existing LLMs still face t…
QA-Merging: Query-Adaptive Reasoning via Layer Selective Model Merging
Zhaofeng Zhong, Wei Yuan, Tong Chen +4
Recent large reasoning models (LRMs) have achieved strong performance on complex reasoning tasks by generating a long chain-of-thought (Long-CoT). However, such lengthy reasoning i…
Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts
Yunke Qu, Liang Qu, Tong Chen +2
Streaming recommender systems (SRSs) are widely deployed in real-world applications, where user interests shift and new items arrive over time. As a result, effectively capturing u…
AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions
Jianxin Li, Liang Qu, Taotao Cai +13
Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalitie…