5 citations · 17 across the 27 of their papers we have counts for
14 papers · 1 filter
Empowering Compact LLMs with Fusion of Layer-wise Exits for Recommendation
Xurong Liang, Tong Chen, Quoc Viet Hung Nguyen +3
Large language model-based recommender systems (LLM-RSs) have demonstrated remarkable capabilities, but are computationally unsustainable for many real-world applications. Compact…
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…
Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
Zongwei Wang, Min Gao, Hongzhi Yin +5
Large language model-empowered agentic recommender systems (ARS) reformulate recommendation as a multi-turn interaction between a recommender agent and a user agent, enabling itera…
Evolutionary Router Feature Generation for Zero-Shot Graph Anomaly Detection with Mixture-of-Experts
Haiyang Jiang, Tong Chen, Xinyi Gao +3
Zero-shot graph anomaly detection (GAD) has attracted increasing attention recent years, yet the heterogeneity of graph structures, features, and anomaly patterns across graphs mak…
When Text-as-Vision Meets Semantic IDs in Generative Recommendation: An Empirical Study
Shutong Qiao, Wei Yuan, Tong Chen +3
Semantic ID learning is a key interface in Generative Recommendation (GR) models, mapping items to discrete identifiers grounded in side information, most commonly via a pretrained…
Integrating Vision-Centric Text Understanding for Conversational Recommender Systems
Wei Yuan, Shutong Qiao, Tong Chen +3
Conversational Recommender Systems (CRSs) have attracted growing attention for their ability to deliver personalized recommendations through natural language interactions. To more…
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…
Harnessing Large Language Models for Group POI Recommendations
Jing Long, Liang Qu, Junliang Yu +3
The rapid proliferation of Location-Based Social Networks (LBSNs) has underscored the importance of Point-of-Interest (POI) recommendation systems in enhancing user experiences. Wh…
On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective
Hung Vinh Tran, Tong Chen, Guanhua Ye +3
Content-based Recommender Systems (CRSs) play a crucial role in shaping user experiences in e-commerce, online advertising, and personalized recommendations. However, due to the va…
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services
Wei Yuan, Chaoqun Yang, Guanhua Ye +3
Federated sequential recommendation (FedSeqRec) has gained growing attention due to its ability to protect user privacy. Unfortunately, the performance of FedSeqRec is still unsati…
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System
Wei Yuan, Chaoqun Yang, Liang Qu +3
Sequential recommender systems have made significant progress. Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning fo…
Robust Federated Contrastive Recommender System against Model Poisoning Attack
Wei Yuan, Chaoqun Yang, Liang Qu +3
Federated Recommender Systems (FedRecs) have garnered increasing attention recently, thanks to their privacy-preserving benefits. However, the decentralized and open characteristic…
Hide Your Model: A Parameter Transmission-free Federated Recommender System
Wei Yuan, Chaoqun Yang, Liang Qu +3
With the growing concerns regarding user data privacy, Federated Recommender System (FedRec) has garnered significant attention recently due to its privacy-preserving capabilities.…
Decentralized Collaborative Learning Framework for Next POI Recommendation
Jing Long, Tong Chen, Nguyen Quoc Viet Hung +1
Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide th…