45 citations · 55 across the 6 of their papers we have counts for
6 papers
VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation
Shutong Qiao, Wei Yuan, Tong Chen +3
Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization. However, these artificial codes…
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…
Federated Unlearning for On-Device Recommendation
Wei Yuan, Hongzhi Yin, Fangzhao Wu +3
The increasing data privacy concerns in recommendation systems have made federated recommendations (FedRecs) attract more and more attention. Existing FedRecs mainly focus on how t…
Learning to Ask Appropriate Questions in Conversational Recommendation
Xuhui Ren, Hongzhi Yin, Tong Chen +3
Conversational recommender systems (CRSs) have revolutionized the conventional recommendation paradigm by embracing dialogue agents to dynamically capture the fine-grained user pre…
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
Tong Chen, Hongzhi Yin, Jie Ren +3
With the ubiquitous graph-structured data in various applications, models that can learn compact but expressive vector representations of nodes have become highly desirable. Recent…
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Hao Wang, Yanmei Fu, Qinyong Wang +3
Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current rese…