12 papers
Hierarchical Residual Policy Optimization for Generative Recommendations
Kaifeng Guo, Yiming Yang, Jingtong Gao +6
Generative recommenders select items by autoregressively decoding semantic identifiers (SIDs), whose token positions induce a coarse-to-fine hierarchy over the item space. In pract…
Escaping the Euclidean Void: Manifold-Informed Flow Matching for Sequential Recommendation
Dengzhao Fang, Jingtong Gao, Yu Li +2
Conventional recommenders capture users' preferences by optimizing observed user-item relations, whereas continuous generative recommendation additionally learns the trajectory of…
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
Yun Lu, Xiaoyu Shi, Hong Xie +2
Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, existing fairne…
Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration
Jingtong Gao, Ling Pan, Yejing Wang +6
Reinforcement Learning (RL) has become a key approach for enhancing the reasoning capabilities of large language models. However, prevalent RL approaches like proximal policy optim…
HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDs
Dengzhao Fang, Jingtong Gao, Chengcheng Zhu +3
Recommender systems are indispensable for helping users navigate the immense item catalogs of modern online platforms. Recently, generative recommendation has emerged as a promisin…
Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner
Yewen Li, Jingtong Gao, Nan Jiang +7
Auto-bidding is central to computational advertising, achieving notable commercial success by optimizing advertisers' bids within economic constraints. Recently, large generative m…