17 citations · 37 across the 11 of their papers we have counts for
11 papers
On the Equivalence Between Auto-Regressive Next Token Prediction and Full-Item-Vocabulary Maximum Likelihood Estimation in Generative Recommendation--A Short Note
Yusheng Huang, Shuang Yang, Zhaojie Liu +1
Generative recommendation (GR) has emerged as a widely adopted paradigm in industrial sequential recommendation. Current GR systems follow a similar pipeline: tokenization for item…
TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds
Yifeng Zhou, Yuehong Hu, Zhixiang Feng +9
Recommender systems have historically developed along two largely independent paradigms: feature interaction models for modeling correlations among multi-field categorical features…
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…
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…
Generative Recommender with End-to-End Learnable Item Tokenization
Enze Liu, Bowen Zheng, Cheng Ling +3
Generative recommendation systems have gained increasing attention as an innovative approach that directly generates item identifiers for recommendation tasks. Despite their potent…
Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning
Jiakai Tang, Sunhao Dai, Zexu Sun +6
In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. Howeve…
IFA: Interaction Fidelity Attention for Entire Lifelong Behaviour Sequence Modeling
Wenhui Yu, Chao Feng, Yanze Zhang +3
The lifelong user behavior sequence provides abundant information of user preference and gains impressive improvement in the recommendation task, however increases computational co…
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…
A Model-based Multi-Agent Personalized Short-Video Recommender System
Peilun Zhou, Xiaoxiao Xu, Lantao Hu +2
Recommender selects and presents top-K items to the user at each online request, and a recommendation session consists of several sequential requests. Formulating a recommendation…
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…