no code implementations • 29 Jan 2025 • Xiaobei Wang, Shuchang Liu, Qingpeng Cai, Xiang Li, Lantao Hu, Han Li, Guangming Xie
Recent advances in recommender systems have shown that user-system interaction essentially formulates long-term optimization problems, and online reinforcement learning can be adopted to improve recommendation performance.
no code implementations • 22 Oct 2024 • Chang Meng, Chenhao Zhai, Xueliang Wang, Shuchang Liu, Xiaoqiang Feng, Lantao Hu, Xiu Li, Han Li, Kun Gai
These two modules work together to dynamically identify and targeting specific user groups and applying treatments effectively.
no code implementations • 19 Oct 2024 • Han Xu, Taoxing Pan, Zhiqiang Liu, Xiaoxiao Xu, Lantao Hu
To address the problem, we propose a novel variational inference approach, namely Group Prior Sampler Variational Inference (GPSVI), which introduces group preferences as priors to refine latent user interests for tail users.
no code implementations • 9 Sep 2024 • Enze Liu, Bowen Zheng, Cheng Ling, Lantao Hu, Han Li, Wayne Xin Zhao
In order to achieve mutual enhancement between the two components, we propose a recommendation-oriented alignment approach by devising two specific optimization objectives: sequence-item alignment and preference-semantic alignment.
no code implementations • 14 Jul 2024 • Jiakai Tang, Sunhao Dai, Zexu Sun, Xu Chen, Jun Xu, Wenhui Yu, Lantao Hu, Peng Jiang, Han Li
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.
no code implementations • 14 Jun 2024 • Wenhui Yu, Chao Feng, Yanze Zhang, Lantao Hu, Peng Jiang, Han Li
The lifelong user behavior sequence provides abundant information of user preference and gains impressive improvement in the recommendation task, however increases computational consumption significantly.
1 code implementation • 10 Jun 2024 • Ziru Liu, Shuchang Liu, Bin Yang, Zhenghai Xue, Qingpeng Cai, Xiangyu Zhao, Zijian Zhang, Lantao Hu, Han Li, Peng Jiang
Recommender systems aim to fulfill the user's daily demands.
1 code implementation • 20 May 2024 • Nian Li, Xin Ban, Cheng Ling, Chen Gao, Lantao Hu, Peng Jiang, Kun Gai, Yong Li, Qingmin Liao
In this paper, we propose to model user Fatigue in interest learning for sequential Recommendations (FRec).
no code implementations • 3 May 2024 • Peilun Zhou, Xiaoxiao Xu, Lantao Hu, Han Li, Peng Jiang
Recommender selects and presents top-K items to the user at each online request, and a recommendation session consists of several sequential requests.
1 code implementation • 29 Apr 2024 • Zijian Zhang, Shuchang Liu, Jiaao Yu, Qingpeng Cai, Xiangyu Zhao, Chunxu Zhang, Ziru Liu, Qidong Liu, Hongwei Zhao, Lantao Hu, Peng Jiang, Kun Gai
M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives.
no code implementations • 6 Apr 2024 • Yabin Zhang, Wenhui Yu, Erhan Zhang, Xu Chen, Lantao Hu, Peng Jiang, Kun Gai
For the model part, we adopt Generative Pre-training Transformer (GPT) as the sequential recommendation model and design a user modular to capture personalized information.
1 code implementation • 4 Apr 2024 • Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai, Xiangyu Zhao, Kesen Zhao, Lantao Hu, Peng Jiang, Kun Gai
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-term rewards.
1 code implementation • 29 Jan 2024 • Xiaobei Wang, Shuchang Liu, Xueliang Wang, Qingpeng Cai, Lantao Hu, Han Li, Peng Jiang, Kun Gai, Guangming Xie
Furthermore, we show that a reward-based future decomposition strategy can better express the item-wise future impact and improve the recommendation accuracy in the long term.
no code implementations • 6 Oct 2023 • Zhenghai Xue, Qingpeng Cai, Tianyou Zuo, Bin Yang, Lantao Hu, Peng Jiang, Kun Gai, Bo An
One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies.
no code implementations • 11 Aug 2023 • Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, Fei Sun
For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.