Search Results for author: Le Wu

Found 37 papers, 23 papers with code

Graph Bottlenecked Social Recommendation

1 code implementation12 Jun 2024 Yonghui Yang, Le Wu, Zihan Wang, Zhuangzhuang He, Richang Hong, Meng Wang

In this paper, we focus on learning the denoised social structure to facilitate recommendation tasks from an information bottleneck perspective.


Boosting Multimedia Recommendation via Separate Generic and Unique Awareness

1 code implementation12 Jun 2024 Zhuangzhuang He, Zihan Wang, Yonghui Yang, Haoyue Bai, Le Wu

Furthermore, in the distancing module, we aim to distance the unique modalities from the modal-generic so that each modality retains its unique and complementary information.

cross-modal alignment Multimedia recommendation +1

Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis

1 code implementation5 Jun 2024 Dacao Zhang, Kun Zhang, Le Wu, Mi Tian, Richang Hong, Meng Wang

Then, we design a novel attribute-oriented predictor to decouple the sensitive attributes, in which fairness-related sensitive features will be eliminated and other useful information will be retained.

Attribute cognitive diagnosis +1

Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias

1 code implementation31 May 2024 Miaomiao Cai, Lei Chen, Yifan Wang, Haoyue Bai, Peijie Sun, Le Wu, Min Zhang, Meng Wang

To alleviate popularity bias, existing efforts focus on emphasizing unpopular items or separating the correlation between item representations and their popularity.

Collaborative Filtering Contrastive Learning +1

Double Correction Framework for Denoising Recommendation

1 code implementation18 May 2024 Zhuangzhuang He, Yifan Wang, Yonghui Yang, Peijie Sun, Le Wu, Haoyue Bai, Jinqi Gong, Richang Hong, Min Zhang

To tackle the above limitations, we propose a Double Correction Framework for Denoising Recommendation (DCF), which contains two correction components from views of more precise sample dropping and avoiding more sparse data.

Denoising Model Optimization +1

Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering

1 code implementation18 Feb 2024 Peijie Sun, Le Wu, Kun Zhang, Xiangzhi Chen, Meng Wang

Using the graph-based collaborative filtering model as our backbone and following the same data augmentation methods as the existing contrastive learning model SGL, we effectively enhance the performance of the recommendation model.

Collaborative Filtering Contrastive Learning +2

Generative Contrastive Graph Learning for Recommendation

1 code implementation11 Jul 2023 Yonghui Yang, Zhengwei Wu, Le Wu, Kun Zhang, Richang Hong, Zhiqiang Zhang, Jun Zhou, Meng Wang

Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.

Collaborative Filtering Contrastive Learning +3

Description-Enhanced Label Embedding Contrastive Learning for Text Classification

1 code implementation15 Jun 2023 Kun Zhang, Le Wu, Guangyi Lv, Enhong Chen, Shulan Ruan, Jing Liu, Zhiqiang Zhang, Jun Zhou, Meng Wang

Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets.

Contrastive Learning Relation +3

MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation

1 code implementation14 Oct 2022 Kang Liu, Feng Xue, Dan Guo, Le Wu, Shujie Li, Richang Hong

This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences.

Collaborative Filtering Image Classification

Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering

1 code implementation26 Apr 2022 Minghao Zhao, Le Wu, Yile Liang, Lei Chen, Jian Zhang, Qilin Deng, Kai Wang, Xudong Shen, Tangjie Lv, Runze Wu

While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?"

Collaborative Filtering Recommendation Systems

A Review-aware Graph Contrastive Learning Framework for Recommendation

1 code implementation26 Apr 2022 Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang, Yong Li

Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better?

Contrastive Learning Recommendation Systems +1

ProFairRec: Provider Fairness-aware News Recommendation

1 code implementation10 Apr 2022 Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang, Xing Xie

To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data.

Fairness News Recommendation

LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching

no code implementations6 Aug 2021 Kun Zhang, Guangyi Lv, Le Wu, Enhong Chen, Qi Liu, Meng Wang

In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations.

Language Modelling Natural Language Inference +2

Privileged Graph Distillation for Cold Start Recommendation

no code implementations31 May 2021 Shuai Wang, Kun Zhang, Le Wu, Haiping Ma, Richang Hong, Meng Wang

The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.

Attribute Collaborative Filtering +1

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation

1 code implementation16 May 2021 Lei Chen, Le Wu, Kun Zhang, Richang Hong, Meng Wang

Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user.

Collaborative Filtering

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

1 code implementation27 Apr 2021 Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.

Collaborative Filtering Sequential Recommendation

Learning Fair Representations for Recommendation: A Graph-based Perspective

1 code implementation18 Feb 2021 Le Wu, Lei Chen, Pengyang Shao, Richang Hong, Xiting Wang, Meng Wang

For each user, this transformation is achieved under the adversarial learning of a user-centric graph, in order to obfuscate each sensitive feature between both the filtered user embedding and the sub graph structures of this user.

Fairness Recommendation Systems

R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching

no code implementations16 Dec 2020 Kun Zhang, Le Wu, Guangyi Lv, Meng Wang, Enhong Chen, Shulan Ruan

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences.

Relation Relation Classification +1

Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation

no code implementations24 May 2020 Le Wu, Yonghui Yang, Lei Chen, Defu Lian, Richang Hong, Meng Wang

The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item's visual content as input, in order to tackle the new segment problem in the test phase.

Graph Neural Network Transfer Learning

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

2 code implementations28 Jan 2020 Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang

Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data.

Collaborative Filtering Recommendation Systems +1

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

2 code implementations15 Jan 2020 Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, Meng Wang

Recently, we propose a preliminary work of a neural influence diffusion network (i. e., DiffNet) for social recommendation (Diffnet), which models the recursive social diffusion process to capture the higher-order relationships for each user.

Collaborative Filtering

Aesthetic Attributes Assessment of Images

2 code implementations11 Jul 2019 Xin Jin, Le Wu, Geng Zhao, Xiao-Dong Li, Xiaokun Zhang, Shiming Ge, Dongqing Zou, Bin Zhou, Xinghui Zhou

This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute.

Attribute Image Captioning +1

Personalized Multimedia Item and Key Frame Recommendation

no code implementations1 Jun 2019 Le Wu, Lei Chen, Yonghui Yang, Richang Hong, Yong Ge, Xing Xie, Meng Wang

We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior.

Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach

no code implementations30 May 2019 Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W. Zheng, Qi Liu

When making cloth decisions, people usually show preferences for different semantic attributes (e. g., the clothes with v-neck collar).

 Ranked #1 on Recommendation Systems on Amazon Fashion (nDCG@10 (500 Neg. Samples) metric, using extra training data)

Attribute Recommendation Systems

A Neural Influence Diffusion Model for Social Recommendation

2 code implementations20 Apr 2019 Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang

The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues.

Collaborative Filtering Recommendation Systems

Quality-aware Unpaired Image-to-Image Translation

no code implementations15 Mar 2019 Lei Chen, Le Wu, Zhenzhen Hu, Meng Wang

To tackle the above two challenges, in this paper, we propose a unified quality-aware GAN-based framework for unpaired image-to-image translation, where a quality-aware loss is explicitly incorporated by comparing each source image and the reconstructed image at the domain level.

Image Quality Assessment Image-to-Image Translation +1

SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation

no code implementations7 Nov 2018 Le Wu, Peijie Sun, Richang Hong, Yanjie Fu, Xiting Wang, Meng Wang

Based on a classical CF model, the key idea of our proposed model is that we borrow the strengths of GCNs to capture how users' preferences are influenced by the social diffusion process in social networks.

Collaborative Filtering Recommendation Systems

A Hierarchical Attention Model for Social Contextual Image Recommendation

1 code implementation3 Jun 2018 Le Wu, Lei Chen, Richang Hong, Yanjie Fu, Xing Xie, Meng Wang

After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects.

Multi-level Chaotic Maps for 3D Textured Model Encryption

no code implementations25 Sep 2017 Xin Jin, Shuyun Zhu, Le Wu, Geng Zhao, Xiao-Dong Li, Quan Zhou, Huimin Lu

In this work, a multi-level chaotic maps models for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons and textures.

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

2 code implementations23 Aug 2017 Xin Jin, Le Wu, Xiao-Dong Li, Siyu Chen, Siwei Peng, Jingying Chi, Shiming Ge, Chenggen Song, Geng Zhao

Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization).

ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation

2 code implementations7 Oct 2016 Xin Jin, Le Wu, Xiao-Dong Li, Xiaokun Zhang, Jingying Chi, Siwei Peng, Shiming Ge, Geng Zhao, Shuying Li

Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected layers on an large scale database of aesthetic related images: AVA, i. e. \emph{domain adaptation}.

Domain Adaptation General Classification +2

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