1 code implementation • 18 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.
1 code implementation • 18 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.
Ranked #1 on Recommendation Systems on Gowalla
no code implementations • 19 Jul 2023 • Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, Xiaofei Zhu
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
1 code implementation • 11 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.
1 code implementation • 15 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.
no code implementations • 14 Jun 2023 • Kai Shu, Yuchang Zhao, Le Wu, Aiping Liu, Ruobing Qian, Xun Chen
Data augmentation is an intuitive way to solve this problem.
1 code implementation • 13 Feb 2023 • Lei Chen, Le Wu, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, Meng Wang
We augment imbalanced training data towards balanced data distribution to improve fairness.
1 code implementation • 14 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.
1 code implementation • 26 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?"
1 code implementation • 26 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?
1 code implementation • 10 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.
no code implementations • 6 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.
no code implementations • 31 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.
1 code implementation • 16 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.
1 code implementation • 27 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.
1 code implementation • 18 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.
no code implementations • 16 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.
no code implementations • 25 May 2020 • Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, Meng Wang
Therefore, item recommendation and attribute inference have become two main tasks in these platforms.
no code implementations • 24 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.
2 code implementations • 28 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.
2 code implementations • 15 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.
2 code implementations • 11 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.
no code implementations • 1 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.
no code implementations • 30 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)
2 code implementations • 20 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.
no code implementations • 15 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.
no code implementations • 7 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.
1 code implementation • 3 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.
no code implementations • 25 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.
2 code implementations • 23 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).
2 code implementations • 7 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}.