4 papers
Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders
Jun Yin, Bangguo Zhu, Peng Huo +5
Recently, Generative Recommenders (GRs), characterized by a unified end-to-end framework, have exhibited astonishing potential in transforming the recommendation paradigm. Despite…
DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
Xirui Liu, Sihang Zhou, Yanning Hou +6
Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowled…
From Token Generation to Item Ranking: Direct Generative Recommendation with Semantic IDs
Yuanbo Zhao, Ruochen Liu, Senzhang Wang +6
Generative recommendation formulates item recommendation as a token-level generation task, where Semantic IDs (SIDs) represents each item as a sequence of discrete tokens. However,…
Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
Yuanchen Bei, Weizhi Zhang, Siwen Wang +10
AI agents have experienced a paradigm shift, from early dominance by reinforcement learning (RL) to the rise of agents powered by large language models (LLMs), and now further adva…