Search Results for author: Lantao Hu

Found 15 papers, 5 papers with code

Value Function Decomposition in Markov Recommendation Process

no code implementations29 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.

Recommendation Systems

Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation

no code implementations22 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.

Marketing

Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Prediction

no code implementations19 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.

Click-Through Rate Prediction Recommendation Systems +1

End-to-End Learnable Item Tokenization for Generative Recommendation

no code implementations9 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.

Decoder Sequential Recommendation

Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning

no code implementations14 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.

Contrastive Learning Recommendation Systems

IFA: Interaction Fidelity Attention for Entire Lifelong Behaviour Sequence Modeling

no code implementations14 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.

Recommendation Systems

Modeling User Fatigue for Sequential Recommendation

1 code implementation20 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).

Contrastive Learning Sequential Recommendation

A Model-based Multi-Agent Personalized Short-Video Recommender System

no code implementations3 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.

Recommendation Systems Reinforcement Learning (RL) +1

M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework

1 code implementation29 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.

AutoML

RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm

no code implementations6 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.

Natural Language Understanding Sequential Recommendation

Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention

1 code implementation4 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.

Contrastive Learning Multi-Task Learning +2

Future Impact Decomposition in Request-level Recommendations

1 code implementation29 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.

Recommendation Systems

AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement

no code implementations6 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.

Reinforcement Learning (RL) Sequential Recommendation

A Large Language Model Enhanced Conversational Recommender System

no code implementations11 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.

Language Modeling Language Modelling +4

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