Search Results for author: Wenjie Wang

Found 47 papers, 26 papers with code

Prospect Personalized Recommendation on Large Language Model-based Agent Platform

1 code implementation28 Feb 2024 Jizhi Zhang, Keqin Bao, Wenjie Wang, Yang Zhang, Wentao Shi, Wanhong Xu, Fuli Feng, Tat-Seng Chua

Additionally, we prospect the evolution of Rec4Agentverse and conceptualize it into three stages based on the enhancement of the interaction and information exchange among Agent Items, Agent Recommender, and the user.

Language Modelling Large Language Model +1

DiFashion: Towards Personalized Outfit Generation and Recommendation

no code implementations27 Feb 2024 Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang, Xiangnan He

The evolution of Outfit Recommendation (OR) in the realm of fashion has progressed through two distinct phases: Pre-defined Outfit Recommendation and Personalized Outfit Composition.

Item-side Fairness of Large Language Model-based Recommendation System

no code implementations23 Feb 2024 Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, Xiangnan He

Towards this goal, we develop a concise and effective framework called IFairLRS to enhance the item-side fairness of an LRS.

Fairness Language Modelling +2

Generative Cross-Modal Retrieval: Memorizing Images in Multimodal Language Models for Retrieval and Beyond

no code implementations16 Feb 2024 Yongqi Li, Wenjie Wang, Leigang Qu, Liqiang Nie, Wenjie Li, Tat-Seng Chua

Building upon this capability, we propose to enable multimodal large language models (MLLMs) to memorize and recall images within their parameters.

Cross-Modal Retrieval Retrieval

Distillation Enhanced Generative Retrieval

no code implementations16 Feb 2024 Yongqi Li, Zhen Zhang, Wenjie Wang, Liqiang Nie, Wenjie Li, Tat-Seng Chua

Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target.

Retrieval Text Retrieval

LLM-based Federated Recommendation

no code implementations15 Feb 2024 Jujia Zhao, Wenjie Wang, Chen Xu, Zhaochun Ren, See-Kiong Ng, Tat-Seng Chua

Nevertheless, applying Fed4Rec to LLM-based recommendation presents two main challenges: first, an increase in the imbalance of performance across clients, affecting the system's efficiency over time, and second, a high demand on clients' computational and storage resources for local training and inference of LLMs.

Federated Learning Language Modelling +2

Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction

1 code implementation6 Feb 2024 Jinqiu Jin, Sihao Ding, Wenjie Wang, Fuli Feng

We conduct a theoretical analysis of the learning process for the weights in the linear component, revealing how group-wise properties of training data influence them.

Blocking Click-Through Rate Prediction

Data-efficient Fine-tuning for LLM-based Recommendation

no code implementations30 Jan 2024 Xinyu Lin, Wenjie Wang, Yongqi Li, Shuo Yang, Fuli Feng, Yinwei Wei, Tat-Seng Chua

To pursue the two objectives, we propose a novel data pruning method based on two scores, i. e., influence score and effort score, to efficiently identify the influential samples.

GOODAT: Towards Test-time Graph Out-of-Distribution Detection

1 code implementation10 Jan 2024 Luzhi Wang, Dongxiao He, He Zhang, Yixin Liu, Wenjie Wang, Shirui Pan, Di Jin, Tat-Seng Chua

To identify and reject OOD samples with GNNs, recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN.

Out-of-Distribution Detection Test

Temporally and Distributionally Robust Optimization for Cold-Start Recommendation

1 code implementation15 Dec 2023 Xinyu Lin, Wenjie Wang, Jujia Zhao, Yongqi Li, Fuli Feng, Tat-Seng Chua

They learn a feature extractor on warm-start items to align feature representations with interactions, and then leverage the feature extractor to extract the feature representations of cold-start items for interaction prediction.

Collaborative Filtering

Do LLMs Implicitly Exhibit User Discrimination in Recommendation? An Empirical Study

no code implementations13 Nov 2023 Chen Xu, Wenjie Wang, Yuxin Li, Liang Pang, Jun Xu, Tat-Seng Chua

Recently, Large Language Models (LLMs) have enhanced user interaction, enabling seamless information retrieval and recommendations.

Information Retrieval Recommendation Systems +1

Attack Prompt Generation for Red Teaming and Defending Large Language Models

1 code implementation19 Oct 2023 Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, Xiangnan He

Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework to enhance their safety against red teaming attacks.

In-Context Learning

A Multi-facet Paradigm to Bridge Large Language Model and Recommendation

no code implementations10 Oct 2023 Xinyu Lin, Wenjie Wang, Yongqi Li, Fuli Feng, See-Kiong Ng, Tat-Seng Chua

To combat these issues, we propose a novel multi-facet paradigm, namely TransRec, to bridge the LLMs to recommendation.

Attribute Language Modelling +2

RecAD: Towards A Unified Library for Recommender Attack and Defense

1 code implementation9 Sep 2023 Changsheng Wang, Jianbai Ye, Wenjie Wang, Chongming Gao, Fuli Feng, Xiangnan He

Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments.

Benchmarking Recommendation Systems

A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems

1 code implementation16 Aug 2023 Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yancheng Luo, Chong Chen, Fuli Feng, Qi Tian

As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a crucial role in augmenting their effectiveness in providing recommendations.

Collaborative Filtering Recommendation Systems

General Debiasing for Multimodal Sentiment Analysis

1 code implementation20 Jul 2023 Teng Sun, Juntong Ni, Wenjie Wang, Liqiang Jing, Yinwei Wei, Liqiang Nie

To this end, we propose a general debiasing framework based on Inverse Probability Weighting (IPW), which adaptively assigns small weights to the samples with larger bias (i. e., the severer spurious correlations).

Multimodal Sentiment Analysis

Robust Prompt Optimization for Large Language Models Against Distribution Shifts

no code implementations23 May 2023 Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao, Jizhi Zhang, Tat-Seng Chua

In this light, we propose a new problem of robust prompt optimization for LLMs against distribution shifts, which requires the prompt optimized over the labeled source group can simultaneously generalize to an unlabeled target group.

Language Modelling Large Language Model

Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation

1 code implementation12 May 2023 Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He

The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM).

Fairness Language Modelling +1

TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

1 code implementation30 Apr 2023 Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He

We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples.

Domain Generalization In-Context Learning +3

Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation

1 code implementation26 Apr 2023 Yang Zhang, Tianhao Shi, Fuli Feng, Wenjie Wang, Dingxian Wang, Xiangnan He, Yongdong Zhang

However, such a manner inevitably learns unstable feature interactions, i. e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving.

Click-Through Rate Prediction Disentanglement +1

Learnable Pillar-based Re-ranking for Image-Text Retrieval

1 code implementation25 Apr 2023 Leigang Qu, Meng Liu, Wenjie Wang, Zhedong Zheng, Liqiang Nie, Tat-Seng Chua

Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities.

Re-Ranking Retrieval +1

Diffusion Recommender Model

1 code implementation11 Apr 2023 Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, Tat-Seng Chua

In light of the impressive advantages of Diffusion Models (DMs) over traditional generative models in image synthesis, we propose a novel Diffusion Recommender Model (named DiffRec) to learn the generative process in a denoising manner.

Denoising Image Generation +1

Generative Recommendation: Towards Next-generation Recommender Paradigm

1 code implementation7 Apr 2023 Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Tat-Seng Chua

However, such a retrieval-based recommender paradigm faces two limitations: 1) the human-generated items in the corpus might fail to satisfy the users' diverse information needs, and 2) users usually adjust the recommendations via inefficient passive feedback, e. g., clicks.

Recommendation Systems Retrieval +1

Causal Disentangled Recommendation Against User Preference Shifts

1 code implementation28 Mar 2023 Wenjie Wang, Xinyu Lin, Liuhui Wang, Fuli Feng, Yunshan Ma, Tat-Seng Chua

Inspired by the causal graph, our key considerations to handle preference shifts lie in modeling the interaction generation procedure by: 1) capturing the preference shifts across environments for accurate preference prediction, and 2) disentangling the sparse influence from user preference to interactions for accurate effect estimation of preference.

Recommendation Systems

Wasserstein Adversarial Examples on Univariant Time Series Data

no code implementations22 Mar 2023 Wenjie Wang, Li Xiong, Jian Lou

In this work, we propose adversarial examples in the Wasserstein space for time series data for the first time and utilize Wasserstein distance to bound the perturbation between normal examples and adversarial examples.

Adversarial Attack Time Series

AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network

1 code implementation8 Jan 2023 Guanghui Zhu, Zhennan Zhu, Wenjie Wang, Zhuoer Xu, Chunfeng Yuan, Yihua Huang

Moreover, to improve the performance of the downstream graph learning task, attribute completion and the training of the heterogeneous GNN should be jointly optimized rather than viewed as two separate processes.

Attribute Graph Learning +1

Causal Intervention for Fairness in Multi-behavior Recommendation

no code implementations10 Sep 2022 Xi Wang, Wenjie Wang, Fuli Feng, Wenge Rong, Chuantao Yin

Specifically, we find that: 1) item popularity is a confounder between the exposed items and users' post-click interactions, leading to the first unfairness; and 2) some hidden confounders (e. g., the reputation of item producers) affect both item popularity and quality, resulting in the second unfairness.

Fairness Recommendation Systems

Causal Inference in Recommender Systems: A Survey and Future Directions

1 code implementation26 Aug 2022 Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, Yong Li

Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction.

Causal Inference Click-Through Rate Prediction +2

Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis

1 code implementation24 Jul 2022 Teng Sun, Wenjie Wang, Liqiang Jing, Yiran Cui, Xuemeng Song, Liqiang Nie

Inspired by this, we devise a model-agnostic counterfactual framework for multimodal sentiment analysis, which captures the direct effect of textual modality via an extra text model and estimates the indirect one by a multimodal model.

counterfactual Counterfactual Inference +2

A Conditional Linear Combination Test with Many Weak Instruments

no code implementations22 Jul 2022 Dennis Lim, Wenjie Wang, Yichong Zhang

Under strong identification, our linear combination test has optimal power against local alternatives among the class of invariant or unbiased tests which are constructed based on jackknife AR and LM tests.

Test

Learning Robust Recommender from Noisy Implicit Feedback

1 code implementation2 Dec 2021 Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua

Inspired by this observation, we propose a new training strategy named Adaptive Denoising Training (ADT), which adaptively prunes the noisy interactions by two paradigms (i. e., Truncated Loss and Reweighted Loss).

Denoising Recommendation Systems

Two Birds, One Stone: Achieving both Differential Privacy and Certified Robustness for Pre-trained Classifiers via Input Perturbation

no code implementations29 Sep 2021 Pengfei Tang, Wenjie Wang, Xiaolan Gu, Jian Lou, Li Xiong, Ming Li

To solve this challenge, a reconstruction network is built before the public pre-trained classifiers to offer certified robustness and defend against adversarial examples through input perturbation.

Image Classification

Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters

no code implementations31 Aug 2021 Wenjie Wang, Yichong Zhang

We study the wild bootstrap inference for instrumental variable regressions in the framework of a small number of large clusters in which the number of clusters is viewed as fixed and the number of observations for each cluster diverges to infinity.

Test

FREE: Feature Refinement for Generalized Zero-Shot Learning

1 code implementation ICCV 2021 Shiming Chen, Wenjie Wang, Beihao Xia, Qinmu Peng, Xinge You, Feng Zheng, Ling Shao

FREE employs a feature refinement (FR) module that incorporates \textit{semantic$\rightarrow$visual} mapping into a unified generative model to refine the visual features of seen and unseen class samples.

Generalized Zero-Shot Learning

Deconfounded Recommendation for Alleviating Bias Amplification

1 code implementation22 May 2021 Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, Tat-Seng Chua

In this work, we scrutinize the cause-effect factors for bias amplification, identifying the main reason lies in the confounder effect of imbalanced item distribution on user representation and prediction score.

Fairness Recommendation Systems

DyHCN: Dynamic Hypergraph Convolutional Networks

no code implementations1 Jan 2021 Nan Yin, Zhigang Luo, Wenjie Wang, Fuli Feng, Xiang Zhang

In general, DyHCN consists of a Hypergraph Convolution (HC) to encode the hypergraph structure at a time point and a Temporal Evolution module (TE) to capture the varying of the relations.

Market2Dish: Health-aware Food Recommendation

1 code implementation11 Dec 2020 Wenjie Wang, Ling-Yu Duan, Hao Jiang, Peiguang Jing, Xuemeng Song, Liqiang Nie

With the rising incidence of some diseases, such as obesity and diabetes, a healthy diet is arousing increasing attention.

Food recommendation Nutrition +1

Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue

1 code implementation21 Sep 2020 Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, Tat-Seng Chua

However, we argue that there is a significant gap between clicks and user satisfaction -- it is common that a user is "cheated" to click an item by the attractive title/cover of the item.

Click-Through Rate Prediction counterfactual +1

Survival Modeling of Suicide Risk with Rare and Uncertain Diagnoses

no code implementations5 Sep 2020 Wenjie Wang, Chongliang Luo, Robert H. Aseltine, Fei Wang, Jun Yan, Kun Chen

Motivated by the pressing need for suicide prevention through improving behavioral healthcare, we use medical claims data to study the risk of subsequent suicide attempts for patients who were hospitalized due to suicide attempts and later discharged.

Survival Analysis

CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial Network

1 code implementation21 Aug 2020 Shiming Chen, Wenjie Wang, Beihao Xia, Xinge You, Zehong Cao, Weiping Ding

In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multi-objective optimization.

GAN image forensics Generative Adversarial Network +1

Denoising Implicit Feedback for Recommendation

1 code implementation7 Jun 2020 Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua

In this work, we explore the central theme of denoising implicit feedback for recommender training.

Denoising Recommendation Systems

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