no code implementations • Findings (EMNLP) 2021 • Shuxian Bi, Chaozhuo Li, Xiao Han, Zheng Liu, Xing Xie, Haizhen Huang, Zengxuan Wen
As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value.
1 code implementation • 14 May 2024 • Rui Li, Chaozhuo Li, Yanming Shen, Zeyu Zhang, Xu Chen
Recent advances in knowledge graph embedding (KGE) rely on Euclidean/hyperbolic orthogonal relation transformations to model intrinsic logical patterns and topological structures.
no code implementations • 30 Mar 2024 • Ziyi Zhou, XiaoMing Zhang, Litian Zhang, Jiacheng Liu, Xi Zhang, Chaozhuo Li
Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content.
no code implementations • 26 Feb 2024 • Peiyan Zhang, Chaozhuo Li, Liying Kang, Feiran Huang, Senzhang Wang, Xing Xie, Sunghun Kim
Moreover, we show that existing contrastive objective learns the low-frequency component of the augmentation graph and propose a high-frequency component (HFC)-aware contrastive learning objective that makes the learned embeddings more distinctive.
no code implementations • 28 Aug 2023 • Peiyan Zhang, Yuchen Yan, Xi Zhang, Chaozhuo Li, Senzhang Wang, Feiran Huang, Sunghun Kim
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs.
no code implementations • 21 Aug 2023 • Peiyan Zhang, Haoyang Liu, Chaozhuo Li, Xing Xie, Sunghun Kim, Haohan Wang
Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in the real world is still in discussion.
no code implementations • 5 Aug 2023 • Hao Wang, Jianxun Lian, Mingqi Wu, Haoxuan Li, Jiajun Fan, Wanyue Xu, Chaozhuo Li, Xing Xie
Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behavior sequences.
1 code implementation • 23 May 2023 • Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie
Embedding models have shown great power in knowledge graph completion (KGC) task.
1 code implementation • 23 May 2023 • Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, Sunghun Kim
Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.
no code implementations • 23 Feb 2023 • Yingze Xie, Jie Xu, LiQiang Qiao, Yun Liu, Feiren Huang, Chaozhuo Li
Sentiment transfer aims at revising the input text to satisfy a given sentiment polarity while retaining the original semantic content.
2 code implementations • 26 Oct 2022 • Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang
In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM.
Ranked #1 on Node Property Prediction on ogbn-papers100M
1 code implementation • 18 Oct 2022 • Zhoujin Tian, Chaozhuo Li, Shuo Ren, Zhiqiang Zuo, Zengxuan Wen, Xinyue Hu, Xiao Han, Haizhen Huang, Denvy Deng, Qi Zhang, Xing Xie
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages.
no code implementations • 17 Oct 2022 • Yiqi Wang, Chaozhuo Li, Wei Jin, Rui Li, Jianan Zhao, Jiliang Tang, Xing Xie
To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.
1 code implementation • 31 Aug 2022 • Qihua Feng, Peiya Li, Zhixun Lu, Chaozhuo Li, Zefang Wang, Zhiquan Liu, Chunhui Duan, Feiran Huang
To this end, image-encryption-based privacy-preserving image retrieval schemes have been developed, which first extract features from cipher-images, and then build retrieval models based on these features.
no code implementations • 2 Aug 2022 • Yiding Zhang, Chaozhuo Li, Senzhang Wang, Jianxun Lian, Xing Xie
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests.
1 code implementation • 26 Jun 2022 • Peiyan Zhang, Jiayan Guo, Chaozhuo Li, Yueqi Xie, Jaeboum Kim, Yan Zhang, Xing Xie, Haohan Wang, Sunghun Kim
Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process.
1 code implementation • 26 Jun 2022 • Jiayan Guo, Peiyan Zhang, Chaozhuo Li, Xing Xie, Yan Zhang, Sunghun Kim
Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions.
1 code implementation • 28 May 2022 • Zhongyu Huang, Yingheng Wang, Chaozhuo Li, Huiguang He
We prove that our approach is strictly more powerful than the 2-dimensional Weisfeiler-Lehman (2-WL) graph isomorphism test and not less powerful than the 3-WL test.
1 code implementation • 22 May 2022 • Xinyan Fan, Jianxun Lian, Wayne Xin Zhao, Zheng Liu, Chaozhuo Li, Xing Xie
We first extract distribution patterns from the item candidates.
1 code implementation • 18 May 2022 • Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Jae Boum Kim, Kai Zhang, Senzhang Wang, Xing Xie, Sunghun Kim
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions.
1 code implementation • 16 Feb 2022 • Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.
2 code implementations • 14 Jan 2022 • Shitao Xiao, Zheng Liu, Weihao Han, Jianjin Zhang, Yingxia Shao, Defu Lian, Chaozhuo Li, Hao Sun, Denvy Deng, Liangjie Zhang, Qi Zhang, Xing Xie
In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification.
no code implementations • 14 Dec 2021 • Yiqi Wang, Chaozhuo Li, Zheng Liu, Mingzheng Li, Jiliang Tang, Xing Xie, Lei Chen, Philip S. Yu
Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations.
1 code implementation • 13 Dec 2021 • Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Jaeboum Kim, Kai Zhang, Senzhang Wang, Sunghun Kim
Our approach leverages bidirectional temporal augmentation and knowledge-enhanced fine-tuning to synthesize authentic pseudo-prior items that \emph{retain user preferences and capture deeper item semantic correlations}, thus boosting the model's expressive power.
no code implementations • 25 Oct 2021 • Jianan Zhao, Chaozhuo Li, Qianlong Wen, Yiqi Wang, Yuming Liu, Hao Sun, Xing Xie, Yanfang Ye
Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases.
no code implementations • 10 Aug 2021 • Yiqi Wang, Chaozhuo Li, Mingzheng Li, Wei Jin, Yuming Liu, Hao Sun, Xing Xie, Jiliang Tang
These methods often make recommendations based on the learned user and item embeddings.
1 code implementation • NeurIPS 2021 • Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, Xing Xie
The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information.
1 code implementation • 25 Apr 2021 • Chaozhuo Li, Bochen Pang, Yuming Liu, Hao Sun, Zheng Liu, Xing Xie, Tianqi Yang, Yanling Cui, Liangjie Zhang, Qi Zhang
Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling.
no code implementations • 22 May 2020 • Yiqi Wang, Yao Ma, Wei Jin, Chaozhuo Li, Charu Aggarwal, Jiliang Tang
Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.
no code implementations • IJCNLP 2019 • Hao Wang, Bing Liu, Chaozhuo Li, Yan Yang, Tianrui Li
We propose a novel DNN model called NetAb (as shorthand for convolutional neural Networks with Ab-networks) to handle noisy labels during training.
no code implementations • 7 Mar 2019 • Chaozhuo Li, Senzhang Wang, Philip S. Yu, Zhoujun Li
Specifically, we propose a MCNE model to learn compact embeddings from pre-learned node features.
no code implementations • COLING 2016 • Chaozhuo Li, Yu Wu, Wei Wu, Chen Xing, Zhoujun Li, Ming Zhou
While automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic context of an input text in the generation process.