From the 1 of 28 linked papers with an AI index.
7 papers · 1 filter
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…
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…
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…
How Do Experts Make Sense of Integrated Process Models?
Tianwa Chen, Barbara Weber, Graeme Shanks +3
A range of integrated modeling approaches have been developed to enable a holistic representation of business process logic together with all relevant business rules. These approac…
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…
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…