5 citations · 14 across the 23 of their papers we have counts for
26 papers
Personalized Communication Skills for Agentic Recommender Systems
Zongwei Wang, Min Gao, Guangyu Hu +2
Agentic recommender systems increasingly employ large language model-based UserAgents to evaluate candidate items through simulated feedback before recommendations are delivered. H…
Where Reasoning Matters: Rethinking Latent Reasoning in Semantic ID-based Generative Recommendation
Shangxin Yang, Min Gao, Zongwei Wang +1
Semantic ID-based generative recommendation predicts an item by generating a short sequence of semantic ID tokens, where each token is produced autoregressively. Latent reasoning h…
Disagreement as Signals: Dual-view Calibration for Sequential Recommendation Denoising
Sijia Li, Min Gao, Zongwei Wang +3
Sequential recommendation seeks to model the evolution of user interests by capturing temporal user intent and item-level transition patterns. Transformer-based recommenders demons…
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…
Green-Red Watermarking for Recommender Systems
Lei Zhou, Min Gao, Zongwei Wang +2
The widespread open-sourcing of advanced recommendation algorithms and the rising threat of model extraction attacks have made safeguarding the intellectual property of recommender…
Multi-LLM Token Filtering and Routing for Sequential Recommendation
Wuhan Chen, Min Gao, Xin Xia +3
Large language models (LLMs) have recently shown promise in recommendation by providing rich semantic knowledge. While most existing approaches rely on external textual corpora to…
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…
DisenReason: Behavior Disentanglement and Latent Reasoning for Shared-Account Sequential Recommendation
Jiawei Cheng, Min Gao, Zongwei Wang +5
Shared-account usage is common on streaming and e-commerce platforms, where multiple users share one account. Existing shared-account sequential recommendation (SSR) methods often…
PAMAS: Self-Adaptive Multi-Agent System with Perspective Aggregation for Misinformation Detection
Zongwei Wang, Min Gao, Junliang Yu +2
Misinformation on social media poses a critical threat to information credibility, as its diverse and context-dependent nature complicates detection. Large language model-empowered…
Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection
Bincheng Gu, Min Gao, Junliang Yu +4
Early detection of fake news is critical for mitigating its rapid dissemination on social media, which can severely undermine public trust and social stability. Recent advancements…
When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents
Zongwei Wang, Bincheng Gu, Hongyu Yu +5
This paper reveals that LLM-powered agents exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias under minimal "us" versus "them" cues. When such grou…
Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection
Chi Wang, Min Gao, Zongwei Wang +3
With the rapid development of large language models, the generation of fake news has become increasingly effortless, posing a growing societal threat and underscoring the urgent ne…
Generative Bid Shading in Real-Time Bidding Advertising
Yinqiu Huang, Hao Ma, Wenshuai Chen +7
Bid shading plays a crucial role in Real-Time Bidding (RTB) by adaptively adjusting the bid to avoid advertisers overspending. Existing mainstream two-stage methods, which first mo…
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…
Breaking the Clusters: Uniformity-Optimization for Text-Based Sequential Recommendation
Wuhan Chen, Zongwei Wang, Min Gao +3
Traditional sequential recommendation (SR) methods heavily rely on explicit item IDs to capture user preferences over time. This reliance introduces critical limitations in cold-st…
Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection
Junwei Yin, Min Gao, Kai Shu +3
The rapid proliferation of fake news on social media threatens social stability, creating an urgent demand for more effective detection methods. While many promising approaches hav…
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…
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…
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…
Efficient Bi-Level Optimization for Recommendation Denoising
Zongwei Wang, Min Gao, Wentao Li +3
The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, impli…
Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation
Yinghui Tao, Min Gao, Junliang Yu +3
Self-supervised learning (SSL) recently has achieved outstanding success on recommendation. By setting up an auxiliary task (either predictive or contrastive), SSL can discover sup…
Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion
Shiqi Wang, Chongming Gao, Min Gao +3
With the increasingly fierce market competition, offering a free trial has become a potent stimuli strategy to promote products and attract users. By providing users with opportuni…