25 citations · 66 across the 13 of their papers we have counts for
9 papers · 1 filter
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…
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…
From Principles to Applications: A Comprehensive Survey of Discrete Tokenizers in Generation, Comprehension, Recommendation, and Information Retrieval
Jian Jia, Jingtong Gao, Ben Xue +6
Discrete tokenizers have emerged as indispensable components in modern machine learning systems, particularly within the context of autoregressive modeling and large language model…
LLM-Powered User Simulator for Recommender System
Zijian Zhang, Shuchang Liu, Ziru Liu +6
User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerati…
Modeling User Retention through Generative Flow Networks
Ziru Liu, Shuchang Liu, Bin Yang +7
Recommender systems aim to fulfill the user's daily demands. While most existing research focuses on maximizing the user's engagement with the system, it has recently been pointed…
M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework
Zijian Zhang, Shuchang Liu, Jiaao Yu +9
Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehen…
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
Ziru Liu, Shuchang Liu, Zijian Zhang +6
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-t…
KuaiSim: A Comprehensive Simulator for Recommender Systems
Kesen Zhao, Shuchang Liu, Qingpeng Cai +5
Reinforcement Learning (RL)-based recommender systems (RSs) have garnered considerable attention due to their ability to learn optimal recommendation policies and maximize long-ter…