Search Results for author: Chaozhuo Li

Found 33 papers, 18 papers with code

Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search

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.

Marketing

GPT4Rec: Graph Prompt Tuning for Streaming Recommendation

no code implementations12 Jun 2024 Peiyan Zhang, Yuchen Yan, Xi Zhang, Liying Kang, Chaozhuo Li, Feiran Huang, Senzhang Wang, Sunghun Kim

Secondly, structure-level prompts guide the model in adapting to broader patterns of connectivity and relationships within the graph.

Graph Learning Recommendation Systems

Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization

1 code implementation14 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.

Knowledge Graph Embedding Knowledge Graphs

FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection

1 code implementation30 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.

Domain Adaptation Fake News Detection

High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs

no code implementations26 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.

Contrastive Learning Representation Learning

TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems

1 code implementation28 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.

Collaborative Filtering Graph Classification +2

Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models

no code implementations21 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.

Image Classification

ConvFormer: Revisiting Transformer for Sequential User Modeling

no code implementations5 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.

Recommendation Systems

Continual Learning on Dynamic Graphs via Parameter Isolation

1 code implementation23 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.

Continual Learning Graph Learning

Generative Sentiment Transfer via Adaptive Masking

no code implementations23 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.

Language Modelling

Learning on Large-scale Text-attributed Graphs via Variational Inference

2 code implementations26 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.

Variational Inference

Test-Time Training for Graph Neural Networks

no code implementations17 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.

Graph Classification Self-Supervised Learning

EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing

1 code implementation31 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.

Cloud Computing Contrastive Learning +4

Geometric Interaction Augmented Graph Collaborative Filtering

no code implementations2 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.

Collaborative Filtering

Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network

1 code implementation26 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.

Session-Based Recommendations

Going Deeper into Permutation-Sensitive Graph Neural Networks

1 code implementation28 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.

HousE: Knowledge Graph Embedding with Householder Parameterization

1 code implementation16 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.

Knowledge Graph Embedding Relation +1

Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval

2 code implementations14 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.

Quantization Retrieval

Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training

1 code implementation13 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.

Data Augmentation Self-Knowledge Distillation +1

Gophormer: Ego-Graph Transformer for Node Classification

no code implementations25 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.

Classification Data Augmentation +4

GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph

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.

Language Modelling Recommendation Systems +1

AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search

1 code implementation25 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.

Marketing

Customized Graph Neural Networks

no code implementations22 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.

General Classification Graph Classification +1

Learning with Noisy Labels for Sentence-level Sentiment 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.

Classification General Classification +4

Multi-Hot Compact Network Embedding

no code implementations7 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.

Network Embedding

Detecting Context Dependent Messages in a Conversational Environment

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.

Chatbot Response Generation

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