20 citations · 32 across the 17 of their papers we have counts for
18 papers
Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging
Linh Dieu Le, Tong Chen, Shazia Sadiq +3
Large language model-based recommender systems are increasingly adopting slow-thinking models that generate step-by-step reasoning before making predictions, often achieving higher…
Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems
Yuchuan Zhao, Tong Chen, Junliang Yu +3
Large language model-powered sequential recommender systems (LLM-SRSs) have recently demonstrated remarkable performance, enabling recommendations through prompt-driven inference o…
ASPIRE: Make Spectral Graph Collaborative Filtering Great Again via Adaptive Filter Learning
Yunhang He, Cong Xu, Zhangchi Zhu +2
Graph filter design is central to spectral collaborative filtering, yet most existing methods rely on manually tuned hyperparameters rather than fully learnable filters. We show th…
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…
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…
When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation
Zongwei Wang, Min Gao, Junliang Yu +3
Graph Contrastive Learning (GCL) has demonstrated substantial promise in enhancing the robustness and generalization of recommender systems, particularly by enabling models to leve…
RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents
Zongwei Wang, Min Gao, Junliang Yu +3
The implicit feedback (e.g., clicks) in real-world recommender systems is often prone to severe noise caused by unintentional interactions, such as misclicks or curiosity-driven be…
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security
Qianru Zhang, Peng Yang, Junliang Yu +4
The widespread adoption of smartphones and Location-Based Social Networks has led to a massive influx of spatio-temporal data, creating unparalleled opportunities for enhancing Poi…
ID-Free Not Risk-Free: LLM-Powered Agents Unveil Risks in ID-Free Recommender Systems
Zongwei Wang, Min Gao, Junliang Yu +4
Recent advances in ID-free recommender systems have attracted significant attention for effectively addressing the cold start problem. However, their vulnerability to malicious att…
Poisoning Attacks and Defenses in Recommender Systems: A Survey
Zongwei Wang, Junliang Yu, Min Gao +4
Modern recommender systems (RS) have profoundly enhanced user experience across digital platforms, yet they face significant threats from poisoning attacks. These attacks, aimed at…
MCRPL: A Pretrain, Prompt & Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation
Hao Liu, Lei Guo, Lei Zhu +3
Cross-domain Recommendation (CR) is the task that tends to improve the recommendations in the sparse target domain by leveraging the information from other rich domains. Existing m…
Poisoning Attacks against Recommender Systems: A Survey
Zongwei Wang, Min Gao, Junliang Yu +3
Modern recommender systems (RS) have seen substantial success, yet they remain vulnerable to malicious activities, notably poisoning attacks. These attacks involve injecting malici…
Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
Lei Guo, Ziang Lu, Junliang Yu +2
Cross-domain Recommendation (CDR) as one of the effective techniques in alleviating the data sparsity issues has been widely studied in recent years. However, previous works may ca…
Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks
Zongwei Wang, Junliang Yu, Min Gao +3
Contrastive learning (CL) has recently gained prominence in the domain of recommender systems due to its great ability to enhance recommendation accuracy and improve model robustne…
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.…
Defense Against Model Extraction Attacks on Recommender Systems
Sixiao Zhang, Hongzhi Yin, Hongxu Chen +1
The robustness of recommender systems has become a prominent topic within the research community. Numerous adversarial attacks have been proposed, but most of them rely on extensiv…
Motif-Based Prompt Learning for Universal Cross-Domain Recommendation
Bowen Hao, Chaoqun Yang, Lei Guo +2
Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of data sparsity and cold start by transferring general knowledge from the source to the target d…
Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation
Xinyi Gao, Wentao Zhang, Junliang Yu +4
Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. However, the sheer size of large-scale graphs presents a significant challenge…