45 citations · 197 across the 23 of their papers we have counts for
20 papers · 1 filter
Denoising Neural Reranker for Recommender Systems
Wenyu Mao, Shuchang Liu, Hailan Yang +9
For multi-stage recommenders in industry, a user request would first trigger a simple and efficient retriever module that selects and ranks a list of relevant items, then the recom…
Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation
Qing Yu, Xiaobei Wang, Shuchang Liu +14
Recommender systems filter contents/items valuable to users by inferring preferences from user features and historical behaviors. Mainstream approaches follow the learning-to-rank…
Comprehensive List Generation for Multi-Generator Reranking
Hailan Yang, Zhenyu Qi, Shuchang Liu +6
Reranking models solve the final recommendation lists that best fulfill users' demands. While existing solutions focus on finding parametric models that approximate optimal policie…
Explicit Uncertainty Modeling for Video Watch Time Prediction
Shanshan Wu, Shuchang Liu, Shuai Zhang +4
In video recommendation, a critical component that determines the system's recommendation accuracy is the watch-time prediction module, since how long a user watches a video direct…
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation
Wenyu Mao, Shuchang Liu, Haoyang Liu +3
Diffusion models (DMs) have emerged as promising approaches for sequential recommendation due to their strong ability to model data distributions and generate high-quality items. E…
Value Function Decomposition in Markov Recommendation Process
Xiaobei Wang, Shuchang Liu, Qingpeng Cai +4
Recent advances in recommender systems have shown that user-system interaction essentially formulates long-term optimization problems, and online reinforcement learning can be adop…
Future-Conditioned Recommendations with Multi-Objective Controllable Decision Transformer
Chongming Gao, Kexin Huang, Ziang Fei +6
Securing long-term success is the ultimate aim of recommender systems, demanding strategies capable of foreseeing and shaping the impact of decisions on future user satisfaction. C…
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…
Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation
Chang Meng, Chenhao Zhai, Xueliang Wang +6
With the rise of short video platforms, video recommendation technology faces more complex challenges. Currently, there are multiple non-personalized modules in the video recommend…
DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems
Jiaju Chen, Chongming Gao, Shuai Yuan +3
The integration of Large Language Models (LLMs) into recommender systems has led to substantial performance improvements. However, this often comes at the cost of diminished recomm…
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…
Future Impact Decomposition in Request-level Recommendations
Xiaobei Wang, Shuchang Liu, Xueliang Wang +6
In recommender systems, reinforcement learning solutions have shown promising results in optimizing the interaction sequence between users and the system over the long-term perform…
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…
A Large Language Model Enhanced Conversational Recommender System
Yue Feng, Shuchang Liu, Zhenghai Xue +5
Conversational recommender systems (CRSs) aim to recommend high-quality items to users through a dialogue interface. It usually contains multiple sub-tasks, such as user preference…
Generative Flow Network for Listwise Recommendation
Shuchang Liu, Qingpeng Cai, Zhankui He +5
Personalized recommender systems fulfill the daily demands of customers and boost online businesses. The goal is to learn a policy that can generate a list of items that matches th…
Multi-Task Recommendations with Reinforcement Learning
Ziru Liu, Jiejie Tian, Qingpeng Cai +8
In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard t…
Exploration and Regularization of the Latent Action Space in Recommendation
Shuchang Liu, Qingpeng Cai, Bowen Sun +7
In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction.…
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement
Wanqi Xue, Qingpeng Cai, Zhenghai Xue +6
Current advances in recommender systems have been remarkably successful in optimizing immediate engagement. However, long-term user engagement, a more desirable performance metric,…