Search Results for author: Lihong Gu

Found 12 papers, 2 papers with code

Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation

no code implementations22 Mar 2024 Shaowei Wei, Zhengwei Wu, Xin Li, Qintong Wu, Zhiqiang Zhang, Jun Zhou, Lihong Gu, Jinjie Gu

Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students).

Clustering Contrastive Learning +3

Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph

no code implementations21 Feb 2024 Qian Zhao, Hao Qian, Ziqi Liu, Gong-Duo Zhang, Lihong Gu

In summary, LLM-KERec addresses the limitations of traditional recommendation systems by incorporating complementary knowledge and utilizing a large language model to capture user intent transitions, adapt to new items, and enhance recommendation efficiency in the evolving e-commerce landscape.

Language Modelling Large Language Model +1

Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning

no code implementations6 Sep 2023 Tianchi Cai, Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Xierui Song, Li Yu, Lihong Gu, Xiaodong Zeng, Jinjie Gu, Guannan Zhang

We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation.

Marketing reinforcement-learning

Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems

no code implementations25 Aug 2023 Tianchi Cai, Shenliao Bao, Jiyan Jiang, Shiji Zhou, Wenpeng Zhang, Lihong Gu, Jinjie Gu, Guannan Zhang

Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards.

Recommendation Systems reinforcement-learning

Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses

2 code implementations Conference 2022 Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, Masashi Sugiyama

\emph{Classification with rejection} (CwR) refrains from making a prediction to avoid critical misclassification when encountering test samples that are difficult to classify.

Classification Multi-class Classification

Adversarial Learning for Incentive Optimization in Mobile Payment Marketing

no code implementations28 Dec 2021 Xuanying Chen, Zhining Liu, Li Yu, Sen Li, Lihong Gu, Xiaodong Zeng, Yize Tan, Jinjie Gu

This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy.


Multi-Objective Online Learning

no code implementations29 Sep 2021 Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Lihong Gu, Xiaodong Zeng, Wenwu Zhu

This paper presents a systematic study of multi-objective online learning.

A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning

no code implementations29 Aug 2021 Tianchi Cai, Wenpeng Zhang, Lihong Gu, Xiaodong Zeng, Jinjie Gu

To apply value-based methods to CRL, a recent groundbreaking line of game-theoretic approaches uses the mixed policy that randomizes among a set of carefully generated policies to converge to the desired constraint-satisfying policy.

General Reinforcement Learning reinforcement-learning +1

Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction

1 code implementation30 Jul 2021 Yun Yue, Yongchao Liu, Suo Tong, Minghao Li, Zhen Zhang, Chunyang Wen, Huanjun Bao, Lihong Gu, Jinjie Gu, Yixiang Mu

We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, AdaHessian, and create a new class of optimizers, which are named Group Momentum, Group Adagrad, Group Adam, Group AMSGrad and Group AdaHessian, etc., accordingly.

Click-Through Rate Prediction

LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign

no code implementations3 Feb 2021 Tianchi Cai, Daxi Cheng, Chen Liang, Ziqi Liu, Lihong Gu, Huizhi Xie, Zhiqiang Zhang, Xiaodong Zeng, Jinjie Gu

In this paper, we analyze the network A/B testing problem under a real-world online marketing campaign, describe our proposed LinkLouvain method, and evaluate it on real-world data.

Link Prediction Marketing

A Reduction Approach to Constrained Reinforcement Learning

no code implementations1 Jan 2021 Tianchi Cai, Wenjie Shi, Lihong Gu, Xiaodong Zeng, Jinjie Gu

In this paper, we present a reduction approach to find sparse policies that randomize among a constant number of policies for the constrained RL problem.

Diversity reinforcement-learning +1

Robust Offline Reinforcement Learning from Low-Quality Data

no code implementations1 Jan 2021 Wenjie Shi, Tianchi Cai, Shiji Song, Lihong Gu, Jinjie Gu, Gao Huang

We theoretically show that AdaPT produces a tight upper bound on the distributional deviation between the learned policy and the behavior policy, and this upper bound is the minimum requirement to guarantee policy improvement at each iteration.

Continuous Control Offline RL +2

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