Search Results for author: Wenqi Fan

Found 40 papers, 19 papers with code

Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias

1 code implementation27 May 2024 Xin He, Wenqi Fan, Ruobing Wang, Yili Wang, Ying Wang, Shirui Pan, Xin Wang

More specifically, CGSoRec first includes a Condition-Guided Social Denoising Model (CSD) to remove redundant social relations in the social network for capturing users' social preferences with items more precisely.


A Survey on RAG Meets LLMs: Towards Retrieval-Augmented Large Language Models

no code implementations10 May 2024 Yujuan Ding, Wenqi Fan, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, Qing Li

Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, retrieval-augmented large language models have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the generation quality of LLMs.

Information Retrieval Retrieval

Graph Machine Learning in the Era of Large Language Models (LLMs)

no code implementations23 Apr 2024 Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li

Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations such as hallucinations and the lack of explainability.

Few-Shot Learning Knowledge Graphs +1

Advancing the Robustness of Large Language Models through Self-Denoised Smoothing

1 code implementation18 Apr 2024 Jiabao Ji, Bairu Hou, Zhen Zhang, Guanhua Zhang, Wenqi Fan, Qing Li, Yang Zhang, Gaowen Liu, Sijia Liu, Shiyu Chang

Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerable concerns.

Graph Unlearning with Efficient Partial Retraining

no code implementations12 Mar 2024 Jiahao Zhang, Lin Wang, Shijie Wang, Wenqi Fan

Graph Neural Networks (GNNs) have achieved remarkable success in various real-world applications.

FashionReGen: LLM-Empowered Fashion Report Generation

no code implementations11 Mar 2024 Yujuan Ding, Yunshan Ma, Wenqi Fan, Yige Yao, Tat-Seng Chua, Qing Li

Fashion analysis refers to the process of examining and evaluating trends, styles, and elements within the fashion industry to understand and interpret its current state, generating fashion reports.

Large Language Models are In-Context Molecule Learners

1 code implementation7 Mar 2024 Jiatong Li, Wei Liu, Zhihao Ding, Wenqi Fan, Yuqiang Li, Qing Li

Specifically, ICMA incorporates the following three stages: Hybrid Context Retrieval, Post-retrieval Re-ranking, and In-context Molecule Tuning.

Cross-Modal Retrieval Re-Ranking +2

Linear-Time Graph Neural Networks for Scalable Recommendations

1 code implementation21 Feb 2024 Jiahao Zhang, Rui Xue, Wenqi Fan, Xin Xu, Qing Li, Jian Pei, Xiaorui Liu

In this paper, we propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches while maintaining GNNs' powerful expressiveness for superior prediction accuracy.

Graph Neural Network Recommendation Systems

A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation

2 code implementations29 Jan 2024 Mohammad Hashemi, Shengbo Gong, Juntong Ni, Wenqi Fan, B. Aditya Prakash, Wei Jin

In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation.

BooleanOCT: Optimal Classification Trees based on multivariate Boolean Rules

1 code implementation29 Jan 2024 Jiancheng Tu, Wenqi Fan, Zhibin Wu

While existing optimal classification trees substantially enhance accuracy over greedy-based tree models like CART, they still fall short when compared to the more complex black-box models, such as random forests.


Untargeted Black-box Attacks for Social Recommendations

no code implementations13 Nov 2023 Wenqi Fan, Shijie Wang, Xiao-Yong Wei, Xiaowei Mei, Qing Li

To perform untargeted attacks on social recommender systems, attackers can construct malicious social relationships for fake users to enhance the attack performance.

Decision Making Multi-agent Reinforcement Learning +1

Fast Graph Condensation with Structure-based Neural Tangent Kernel

1 code implementation17 Oct 2023 Lin Wang, Wenqi Fan, Jiatong Li, Yao Ma, Qing Li

The rapid development of Internet technology has given rise to a vast amount of graph-structured data.

Dataset Condensation Graph Mining

Dataset Condensation for Recommendation

no code implementations2 Oct 2023 Jiahao Wu, Wenqi Fan, Shengcai Liu, Qijiong Liu, Rui He, Qing Li, Ke Tang

However, applying existing approaches to condense recommendation datasets is impractical due to following challenges: (i) sampling-based methods are inadequate in addressing the long-tailed distribution problem; (ii) synthesizing-based methods are not applicable due to discreteness of interactions and large size of recommendation datasets; (iii) neither of them fail to address the specific issue in recommendation of false negative items, where items with potential user interest are incorrectly sampled as negatives owing to insufficient exposure.

Dataset Condensation

Revisiting Link Prediction: A Data Perspective

1 code implementation1 Oct 2023 Haitao Mao, Juanhui Li, Harry Shomer, Bingheng Li, Wenqi Fan, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang

We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.

Link Prediction

Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling

no code implementations22 Sep 2023 Jiahao Wu, Wenqi Fan, Shengcai Liu, Qijiong Liu, Qing Li, Ke Tang

To model the compatibility between user intents and item properties, we design the user-item co-clustering module, maximizing the mutual information of co-clusters of users and items.

Collaborative Filtering

Certified Robustness for Large Language Models with Self-Denoising

1 code implementation14 Jul 2023 Zhen Zhang, Guanhua Zhang, Bairu Hou, Wenqi Fan, Qing Li, Sijia Liu, Yang Zhang, Shiyu Chang

This largely falls into the study of certified robust LLMs, i. e., all predictions of LLM are certified to be correct in a local region around the input.


Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

2 code implementations7 Jul 2023 Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, Jiliang Tang

The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding.

General Knowledge Node Classification

Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective

1 code implementation11 Jun 2023 Jiatong Li, Yunqing Liu, Wenqi Fan, Xiao-Yong Wei, Hui Liu, Jiliang Tang, Qing Li

In this work, we propose a novel LLM-based framework (MolReGPT) for molecule-caption translation, where an In-Context Few-Shot Molecule Learning paradigm is introduced to empower molecule discovery with LLMs like ChatGPT to perform their in-context learning capability without domain-specific pre-training and fine-tuning.

In-Context Learning Molecule Captioning +3

Fairly Adaptive Negative Sampling for Recommendations

no code implementations16 Feb 2023 Xiao Chen, Wenqi Fan, Jingfan Chen, Haochen Liu, Zitao Liu, Zhaoxiang Zhang, Qing Li

Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i. e., clicked by a user) and negative items (i. e., obtained by negative sampling).

Attribute Fairness

Generative Diffusion Models on Graphs: Methods and Applications

1 code implementation6 Feb 2023 Chengyi Liu, Wenqi Fan, Yunqing Liu, Jiatong Li, Hang Li, Hui Liu, Jiliang Tang, Qing Li

Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years.

Denoising Graph Generation +2

A Comprehensive Survey on Trustworthy Recommender Systems

no code implementations21 Sep 2022 Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li

As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.

Fairness Recommendation Systems

Fairness Reprogramming

1 code implementation21 Sep 2022 Guanhua Zhang, Yihua Zhang, Yang Zhang, Wenqi Fan, Qing Li, Sijia Liu, Shiyu Chang

Specifically, FairReprogram considers the case where models can not be changed and appends to the input a set of perturbations, called the fairness trigger, which is tuned towards the fairness criteria under a min-max formulation.


Disentangled Contrastive Learning for Social Recommendation

1 code implementation18 Aug 2022 Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu, Qing Li, Ke Tang

In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec.

Contrastive Learning Representation Learning +1

Knowledge-enhanced Black-box Attacks for Recommendations

no code implementations21 Jul 2022 Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao, Chunfeng Yuan, Qing Li, Yihua Huang

Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i. e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items.

Attribute Recommendation Systems

Defense Against Gradient Leakage Attacks via Learning to Obscure Data

no code implementations1 Jun 2022 Yuxuan Wan, Han Xu, Xiaorui Liu, Jie Ren, Wenqi Fan, Jiliang Tang

However, federated learning is still under the risk of privacy leakage because of the existence of attackers who deliberately conduct gradient leakage attacks to reconstruct the client data.

Federated Learning Privacy Preserving

A Comprehensive Survey on Automated Machine Learning for Recommendations

no code implementations4 Apr 2022 Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, Ruiming Tang

Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences.

AutoML BIG-bench Machine Learning +2

Graph Trend Filtering Networks for Recommendations

1 code implementation12 Aug 2021 Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li

The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.

Collaborative Filtering Graph Representation Learning +1

Jointly Attacking Graph Neural Network and its Explanations

no code implementations7 Aug 2021 Wenqi Fan, Wei Jin, Xiaorui Liu, Han Xu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, JianPing Wang, Charu Aggarwal

Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.

Graph Neural Network

Trustworthy AI: A Computational Perspective

no code implementations12 Jul 2021 Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil K. Jain, Jiliang Tang

In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society.


AutoLoss: Automated Loss Function Search in Recommendations

no code implementations12 Jun 2021 Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang

Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.

Recommendation Systems

Attacking Black-box Recommendations via Copying Cross-domain User Profiles

1 code implementation17 May 2020 Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jian-Ping Wang, Jiliang Tang, Qing Li

In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.

Data Poisoning Recommendation Systems

Does Gender Matter? Towards Fairness in Dialogue Systems

1 code implementation COLING 2020 Haochen Liu, Jamell Dacon, Wenqi Fan, Hui Liu, Zitao Liu, Jiliang Tang

In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models.


Deep Social Collaborative Filtering

no code implementations16 Jul 2019 Wenqi Fan, Yao Ma, Dawei Yin, Jian-Ping Wang, Jiliang Tang, Qing Li

Meanwhile, most of these models treat neighbors' information equally without considering the specific recommendations.

Collaborative Filtering Recommendation Systems

Deep Adversarial Social Recommendation

2 code implementations30 May 2019 Wenqi Fan, Tyler Derr, Yao Ma, JianPing Wang, Jiliang Tang, Qing Li

Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life.

Recommendation Systems Representation Learning

Graph Neural Networks for Social Recommendation

8 code implementations19 Feb 2019 Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin

These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.

Ranked #3 on Recommendation Systems on Epinions (using extra training data)

Graph Neural Network Recommendation Systems

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