Search Results for author: Hengliang Luo

Found 7 papers, 5 papers with code

NEON: Living Needs Prediction System in Meituan

no code implementations31 Jul 2023 Xiaochong Lan, Chen Gao, Shiqi Wen, Xiuqi Chen, Yingge Che, Han Zhang, Huazhou Wei, Hengliang Luo, Yong Li

To address these two challenges, we design a system of living NEeds predictiON named NEON, consisting of three phases: feature mining, feature fusion, and multi-task prediction.

The Second-place Solution for CVPR VISION 23 Challenge Track 1 -- Data Effificient Defect Detection

1 code implementation25 Jun 2023 Xian Tao, Zhen Qu, Hengliang Luo, Jianwen Han, Yonghao He, Danfeng Liu, Chengkan Lv, Fei Shen, Zhengtao Zhang

The Vision Challenge Track 1 for Data-Effificient Defect Detection requires competitors to instance segment 14 industrial inspection datasets in a data-defificient setting.

Defect Detection Instance Segmentation +2

Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems

1 code implementation18 May 2023 Zhaochun Ren, Na Huang, Yidan Wang, Pengjie Ren, Jun Ma, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon M Jose, Xin Xin

For the second issue, we propose introducing contrastive signals between augmented states and the state randomly sampled from other sessions to improve the state representation learning further.

Recommendation Systems reinforcement-learning +2

Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment

1 code implementation9 May 2023 Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren, Zhumin Chen, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon Jose, Maarten de Rijke, Zhaochun Ren

Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases.

Denoising Open-Ended Question Answering +2

On the User Behavior Leakage from Recommender System Exposure

1 code implementation16 Oct 2022 Xin Xin, Jiyuan Yang, Hanbing Wang, Jun Ma, Pengjie Ren, Hengliang Luo, Xinlei Shi, Zhumin Chen, Zhaochun Ren

Given the fact that system exposure data could be widely accessed from a relatively larger scope, we believe that the user past behavior privacy has a high risk of leakage in recommender systems.

Recommendation Systems

Debiasing Learning for Membership Inference Attacks Against Recommender Systems

1 code implementation24 Jun 2022 Zihan Wang, Na Huang, Fei Sun, Pengjie Ren, Zhumin Chen, Hengliang Luo, Maarten de Rijke, Zhaochun Ren

To address the above limitations, we propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components: (1) a difference vector generator, (2) a disentangled encoder, (3) a weight estimator, and (4) an attack model.

Recommendation Systems

Cannot find the paper you are looking for? You can Submit a new open access paper.