no code implementations • EMNLP (ClinicalNLP) 2020 • Wenjie Wang, Youngja Park, Taesung Lee, Ian Molloy, Pengfei Tang, Li Xiong
Among the modalities of medical data, the clinical summaries have higher risks to be attacked because they are generated by third-party companies.
no code implementations • GWC 2018 • Laura Slaughter, Wenjie Wang, Luis Morgado Da Costa, Francis Bond
To do this, we propose to build a cross-lingual wordnet within the do-main of theology.
1 code implementation • 27 Apr 2024 • Chen Xu, Xiaopeng Ye, Wenjie Wang, Liang Pang, Jun Xu, Tat-Seng Chua
From a taxation perspective, we theoretically demonstrate that most previous fair re-ranking methods can be reformulated as an item-level tax policy.
no code implementations • 25 Apr 2024 • Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, Tat-Seng Chua
With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs.
no code implementations • 25 Apr 2024 • Yukai Zhou, Wenjie Wang
However, the typical attack in this category GCG has very limited attack success rate.
no code implementations • 16 Apr 2024 • Zhiyu Hu, Yang Zhang, Minghao Xiao, Wenjie Wang, Fuli Feng, Xiangnan He
The evolving paradigm of Large Language Model-based Recom- mendation (LLMRec) customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) using recommenda- tion data.
no code implementations • 8 Apr 2024 • Chengyan Fu, Wenjie Wang
Randomized smoothing is the primary certified robustness method for accessing the robustness of deep learning models to adversarial perturbations in the l2-norm, by adding isotropic Gaussian noise to the input image and returning the majority votes over the base classifier.
no code implementations • 28 Mar 2024 • Xinyu Bian, Yuhao Liu, Yizhou Xu, Tianqi Hou, Wenjie Wang, Yuyi Mao, Jun Zhang
Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, which achieves performance similar to the optimal centralized precoding scheme.
1 code implementation • 25 Mar 2024 • Yue Xu, Wenjie Wang
Prompt-based learning is a new language model training paradigm that adapts the Pre-trained Language Models (PLMs) to downstream tasks, which revitalizes the performance benchmarks across various natural language processing (NLP) tasks.
no code implementations • 15 Mar 2024 • Yuhao Liu, Xinyu Bian, Yizhou Xu, Tianqi Hou, Wenjie Wang, Yuyi Mao, Jun Zhang
In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission.
no code implementations • 15 Mar 2024 • Moxin Li, Wenjie Wang, Fuli Feng, Fengbin Zhu, Qifan Wang, Tat-Seng Chua
Confidence estimation aiming to evaluate output trustability is crucial for the application of large language models (LLM), especially the black-box ones.
no code implementations • 12 Mar 2024 • Mohamed Ragab, Yury Savateev, Wenjie Wang, Reza Moosaei, Thanassis Tiropanis, Alexandra Poulovassilis, Adriane Chapman, Helen Oliver, George Roussos
The DESERE Workshop, our First Workshop on Decentralised Search and Recommendation, offers a platform for researchers to explore and share innovative ideas on decentralised web services, mainly focusing on three major topics: (i) societal impact of decentralised systems: their effect on privacy, policy, and regulation; (ii) decentralising applications: algorithmic and performance challenges that arise from decentralisation; and (iii) infrastructure to support decentralised systems and services: peer-to-peer networks, routing, and performance evaluation tools
no code implementations • 12 Mar 2024 • Shuxian Bi, Wenjie Wang, Hang Pan, Fuli Feng, Xiangnan He
However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization.
no code implementations • 7 Mar 2024 • Wenjie Wang, Yang Zhang, Xinyu Lin, Fuli Feng, Weiwen Liu, Yong liu, Xiangyu Zhao, Wayne Xin Zhao, Yang song, Xiangnan He
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations.
no code implementations • 7 Mar 2024 • Leigang Qu, Wenjie Wang, Yongqi Li, Hanwang Zhang, Liqiang Nie, Tat-Seng Chua
We present a discriminative adapter built on T2I models to probe their discriminative abilities on two representative tasks and leverage discriminative fine-tuning to improve their text-image alignment.
no code implementations • 6 Mar 2024 • Xiaoyan Hu, Pengle Wen, Han Xiao, Wenjie Wang, Kai-Kit Wong
By leveraging the SWIPT technique, the UAV can simultaneously transmit energy and the computing results during the downlink period.
1 code implementation • 5 Mar 2024 • Wenjie Wang, Changsheng Wang, Fuli Feng, Wentao Shi, Daizong Ding, Tat-Seng Chua
UBA estimates the treatment effect on each target user and optimizes the allocation of fake user budgets to maximize the attack performance.
no code implementations • 29 Feb 2024 • Wentao Shi, Chenxu Wang, Fuli Feng, Yang Zhang, Wenjie Wang, Junkang Wu, Xiangnan He
Compared to AUC, LLPAUC considers only the partial area under the ROC curve in the Lower-Left corner to push the optimization focus on Top-K. We provide theoretical validation of the correlation between LLPAUC and Top-K ranking metrics and demonstrate its robustness to noisy user feedback.
1 code implementation • 28 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.
no code implementations • 27 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.
1 code implementation • 23 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.
no code implementations • 16 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.
no code implementations • 16 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.
no code implementations • 15 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.
1 code implementation • 6 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.
no code implementations • 30 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.
no code implementations • 13 Jan 2024 • Jujia Zhao, Wenjie Wang, Yiyan Xu, Teng Sun, Fuli Feng
In the realm of recommender systems, handling noisy implicit feedback is a prevalent challenge.
1 code implementation • 10 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.
1 code implementation • 15 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.
no code implementations • 13 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.
1 code implementation • 19 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.
no code implementations • 10 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.
1 code implementation • 9 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.
no code implementations • 19 Aug 2023 • Kaihang Pan, Juncheng Li, Wenjie Wang, Hao Fei, Hongye Song, Wei Ji, Jun Lin, Xiaozhong Liu, Tat-Seng Chua, Siliang Tang
Recent studies indicate that dense retrieval models struggle to perform well on a wide variety of retrieval tasks that lack dedicated training data, as different retrieval tasks often entail distinct search intents.
1 code implementation • 16 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.
1 code implementation • 20 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).
no code implementations • 23 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.
1 code implementation • 12 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).
1 code implementation • 30 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.
1 code implementation • 26 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.
1 code implementation • 25 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.
1 code implementation • 11 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.
1 code implementation • 7 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.
1 code implementation • 28 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.
no code implementations • 22 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.
1 code implementation • 8 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.
1 code implementation • 8 Dec 2022 • Xinyu Lin, Yiyan Xu, Wenjie Wang, Yang Zhang, Fuli Feng
This objective requires to 1) automatically mask spurious features without supervision, and 2) block the negative effect transmission from spurious features to other features during SSL.
no code implementations • 10 Sep 2022 • Xi Wang, Wenjie Wang, Fuli Feng, Wenge Rong, Chuantao Yin, Zhang Xiong
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.
1 code implementation • 26 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.
1 code implementation • 24 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.
no code implementations • 22 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.
1 code implementation • 29 Apr 2022 • Wenjie Wang, Fuli Feng, Liqiang Nie, Tat-Seng Chua
both accuracy and diversity.
1 code implementation • 2 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).
no code implementations • 29 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.
no code implementations • 31 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.
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.
no code implementations • NAACL 2021 • Wenjie Wang, Pengfei Tang, Jian Lou, Li Xiong
The robustness and security of natural language processing (NLP) models are significantly important in real-world applications.
1 code implementation • 22 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.
no code implementations • 1 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.
1 code implementation • 11 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.
1 code implementation • 21 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.
no code implementations • 5 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.
1 code implementation • 21 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.
1 code implementation • 7 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.