no code implementations • ACL 2022 • Moxin Li, Fuli Feng, Hanwang Zhang, Xiangnan He, Fengbin Zhu, Tat-Seng Chua
Neural discrete reasoning (NDR) has shown remarkable progress in combining deep models with discrete reasoning.
no code implementations • 12 May 2024 • Wenjie Wang, Honghui Bao, Xinyu Lin, Jizhi Zhang, Yongqi Li, Fuli Feng, See-Kiong Ng, Tat-Seng Chua
To address these shortcomings, we propose LETTER (a LEarnable Tokenizer for generaTivE Recommendation), designed to meet the key criteria of identifiers by integrating hierarchical semantics, collaborative signals, and code assignment diversity.
1 code implementation • 2 May 2024 • Tianhao Shi, Yang Zhang, Jizhi Zhang, Fuli Feng, Xiangnan He
To this end, we propose Distributionally Robust Fair Optimization (DRFO), which minimizes the worst-case unfairness over all potential probability distributions of missing sensitive attributes instead of the reconstructed one to account for the impact of the reconstruction errors.
1 code implementation • 30 Apr 2024 • Haoxuan Li, Chunyuan Zheng, Sihao Ding, Peng Wu, Zhi Geng, Fuli Feng, Xiangnan He
On this basis, we propose a novel ideal loss that can be used to deal with selection bias in the presence of neighborhood effect.
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 • 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 • 26 Mar 2024 • Zihao Zhao, Yi Jing, Fuli Feng, Jiancan Wu, Chongming Gao, Xiangnan He
Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information.
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 • 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.
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 • 29 Feb 2024 • Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, Fuli Feng
To this end, we propose a Bi-level Learnable LLM Planner framework, which consists of a set of LLM instances and breaks down the learning process into macro-learning and micro-learning to learn macro-level guidance and micro-level personalized recommendation policies, respectively.
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.
1 code implementation • 27 Feb 2024 • Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang, Xiangnan He
Outfit Recommendation (OR) in the fashion domain has evolved through two stages: 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.
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 • 24 Jan 2024 • Fengbin Zhu, Ziyang Liu, Fuli Feng, Chao Wang, Moxin Li, Tat-Seng Chua
In this work, we address question answering (QA) over a hybrid of tabular and textual data that are very common content on the Web (e. g. SEC filings), where discrete reasoning capabilities are often required.
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.
no code implementations • 25 Dec 2023 • Tianhao Shi, Yang Zhang, Zhijian Xu, Chong Chen, Fuli Feng, Xiangnan He, Qi Tian
Rather than directly dismissing the role of incremental learning, we ascribe this lack of anticipated performance improvement to the mismatch between the LLM4Recarchitecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendation, hampering its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context.
1 code implementation • 18 Dec 2023 • Yimeng Bai, Yang Zhang, Jing Lu, Jianxin Chang, Xiaoxue Zang, Yanan Niu, Yang song, Fuli Feng
Through meta-learning techniques, LabelCraft effectively addresses the bi-level optimization hurdle posed by the recommender and labeling models, enabling the automatic acquisition of intricate label generation mechanisms. Extensive experiments on real-world datasets corroborate LabelCraft's excellence across varied operational metrics, encompassing usage time, user engagement, and retention.
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.
1 code implementation • 7 Dec 2023 • Jaehyung Kim, Yuning Mao, Rui Hou, Hanchao Yu, Davis Liang, Pascale Fung, Qifan Wang, Fuli Feng, Lifu Huang, Madian Khabsa
Under a unified evaluation of fine-tuned LMs by incorporating four representative perspectives of model robustness, we demonstrate the effectiveness of RoAST compared to state-of-the-art fine-tuning methods on six different types of LMs, which indicates its usefulness in practice.
1 code implementation • 30 Oct 2023 • Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang, Xiangnan He
In pursuit of superior recommendations for both cold and warm start scenarios, we introduce CoLLM, an innovative LLMRec methodology that seamlessly incorporates collaborative information into LLMs for recommendation.
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 • 27 Aug 2023 • Yu Wang, Xin Xin, Zaiqiao Meng, Joemon Jose, Fuli Feng
We employ the proposed DeCA on both the binary label scenario and the multiple label scenario.
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 • 3 Aug 2023 • Haoxuan Li, Taojun Hu, Zetong Xiong, Chunyuan Zheng, Fuli Feng, Xiangnan He, Xiao-Hua Zhou
Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety.
no code implementations • 19 Jul 2023 • Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, Xiaofei Zhu
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
no code implementations • 5 Jul 2023 • Yang Zhang, Zhiyu Hu, Yimeng Bai, Fuli Feng, Jiancan Wu, Qifan Wang, Xiangnan He
In this work, we propose an Influence Function-based Recommendation Unlearning (IFRU) framework, which efficiently updates the model without retraining by estimating the influence of the unusable data on the model via the influence function.
no code implementations • 30 Jun 2023 • Yang Zhang, Yimeng Bai, Jianxin Chang, Xiaoxue Zang, Song Lu, Jing Lu, Fuli Feng, Yanan Niu, Yang song
With the proliferation of short video applications, the significance of short video recommendations has vastly increased.
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 • 3 May 2023 • Fengbin Zhu, Chao Wang, Fuli Feng, Zifeng Ren, Moxin Li, Tat-Seng Chua
Discrete reasoning over table-text documents (e. g., financial reports) gains increasing attention in recent two years.
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 • 27 Apr 2023 • Yulong Huang, Yang Zhang, Qifan Wang, Chenxu Wang, Fuli Feng
To improve the accuracy of these models, some researchers have attempted to simulate human analogical reasoning to correct predictions for testing data by drawing analogies with the prediction errors of similar training data.
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 • 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.
1 code implementation • 7 Feb 2023 • Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, Xiangnan He
Secondly, we prove that OPAUC has a stronger connection with Top-K evaluation metrics than AUC and verify it with simulation experiments.
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.
1 code implementation • 27 Nov 2022 • Gang Chen, Jiawei Chen, Fuli Feng, Sheng Zhou, Xiangnan He
Traditional solutions first train a full teacher model from the training data, and then transfer its knowledge (\ie \textit{soft labels}) to supervise the learning of a compact student model.
1 code implementation • 22 Sep 2022 • Chenxu Wang, Fuli Feng, Yang Zhang, Qifan Wang, Xunhan Hu, Xiangnan He
A standard choice is treating the missing data as negative training samples and estimating interaction likelihood between user-item pairs along with the observed interactions.
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.
no code implementations • 17 Aug 2022 • Shengyu Zhang, Bofang Li, Dong Yao, Fuli Feng, Jieming Zhu, Wenyan Fan, Zhou Zhao, Xiaofei He, Tat-Seng Chua, Fei Wu
Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial recommendations (e. g., popular items) or even weird ones that are far beyond users' interests.
1 code implementation • 17 Aug 2022 • Shengyu Zhang, Lingxiao Yang, Dong Yao, Yujie Lu, Fuli Feng, Zhou Zhao, Tat-Seng Chua, Fei Wu
Specifically, Re4 encapsulates three backward flows, i. e., 1) Re-contrast, which drives each interest embedding to be distinct from other interests using contrastive learning; 2) Re-attend, which ensures the interest-item correlation estimation in the forward flow to be consistent with the criterion used in final recommendation; and 3) Re-construct, which ensures that each interest embedding can semantically reflect the information of representative items that relate to the corresponding interest.
no code implementations • 25 Jul 2022 • Fengbin Zhu, Wenqiang Lei, Fuli Feng, Chao Wang, Haozhou Zhang, Tat-Seng Chua
Document Visual Question Answering (VQA) aims to understand visually-rich documents to answer questions in natural language, which is an emerging research topic for both Natural Language Processing and Computer Vision.
no code implementations • 14 Jul 2022 • Weijian Chen, Yixin Cao, Fuli Feng, Xiangnan He, Yongdong Zhang
On the one hand, their performance will dramatically degrade along with the increasing sparsity of KGs.
1 code implementation • ACM SIGIR Conference on Research and Development in Information Retrieval 2022 • Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji, Tat-Seng Chua
Existing approaches either 1) predict structured dialog acts first and then generate natural response; or 2) map conversation context to natural responses directly in an end-to-end manner.
1 code implementation • 22 May 2022 • Liqi Yan, Qifan Wang, Yiming Cui, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu
Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description.
no code implementations • 16 May 2022 • Xinyuan Zhu, Yang Zhang, Fuli Feng, Xun Yang, Dingxian Wang, Xiangnan He
Towards this goal, we propose a Hidden Confounder Removal (HCR) framework that leverages front-door adjustment to decompose the causal effect into two partial effects, according to the mediators between item features and user feedback.
1 code implementation • 13 May 2022 • Xiangnan He, Yang Zhang, Fuli Feng, Chonggang Song, Lingling Yi, Guohui Ling, Yongdong Zhang
We demonstrate DCR on the backbone model of neural factorization machine (NFM), showing that DCR leads to more accurate prediction of user preference with small inference time cost.
no code implementations • 3 May 2022 • Zhenguang Liu, Sifan Wu, Chejian Xu, Xiang Wang, Lei Zhu, Shuang Wu, Fuli Feng
3) To enhance texture details, we encode facial features with geometric guidance and employ local GANs to refine the face, feet, and hands.
1 code implementation • 29 Apr 2022 • Wenjie Wang, Fuli Feng, Liqiang Nie, Tat-Seng Chua
both accuracy and diversity.
1 code implementation • 23 Apr 2022 • Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He, Tat-Seng Chua
Such reward accounts for the dependency of the newly-added edge and the previously-added edges, thus reflecting whether they collaborate together and form a coalition to pursue better explanations.
no code implementations • 5 Feb 2022 • Xiaohe Li, Lijie Wen, Yawen Deng, Fuli Feng, Xuming Hu, Lei Wang, Zide Fan
Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification.
no code implementations • 1 Feb 2022 • Qifan Wang, Yi Fang, Anirudh Ravula, Fuli Feng, Xiaojun Quan, Dongfang Liu
Structure information extraction refers to the task of extracting structured text fields from web pages, such as extracting a product offer from a shopping page including product title, description, brand and price.
no code implementations • 21 Jan 2022 • Ying-Xin Wu, Xiang Wang, An Zhang, Xia Hu, Fuli Feng, Xiangnan He, Tat-Seng Chua
In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.
1 code implementation • 14 Jan 2022 • Zhiyuan Liu, Yixin Cao, Fuli Feng, Xiang Wang, Jie Tang, Kenji Kawaguchi, Tat-Seng Chua
We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free).
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 • Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji, Tat-Seng Chua
The core is to construct a latent content space for strategy optimization and disentangle the surface style from it.
1 code implementation • 16 Aug 2021 • Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi, Yongdong Zhang
Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.
1 code implementation • ACL 2021 • Chen Qian, Fuli Feng, Lijie Wen, Chunping Ma, Pengjun Xie
In inference, given a factual input document, Corsair imagines its two counterfactual counterparts to distill and mitigate the two biases captured by the poisonous model.
1 code implementation • Findings (ACL) 2021 • Fuli Feng, Jizhi Zhang, Xiangnan He, Hanwang Zhang, Tat-Seng Chua
Present language understanding methods have demonstrated extraordinary ability of recognizing patterns in texts via machine learning.
1 code implementation • 3 Jun 2021 • Xun Yang, Fuli Feng, Wei Ji, Meng Wang, Tat-Seng Chua
To fill the research gap, we propose a causality-inspired VMR framework that builds structural causal model to capture the true effect of query and video content on the prediction.
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 • 20 May 2021 • Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon Jose, Fuli Feng
A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.
1 code implementation • ACL 2021 • Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua
In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions.
Ranked #1 on Question Answering on TAT-QA
1 code implementation • 13 May 2021 • Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, Yongdong Zhang
This work studies an unexplored problem in recommendation -- how to leverage popularity bias to improve the recommendation accuracy.
1 code implementation • 5 Mar 2021 • Shunyu Jiang, Fuli Feng, Weijian Chen, Xiang Li, Xiangnan He
Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage. This work explores the potential of metric-based meta-learning for solving few-shot graph classification. We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph.
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 • 29 Oct 2020 • Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, JinFeng Yi, Xiangnan He
Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items.
1 code implementation • 23 Oct 2020 • Hande Dong, Jiawei Chen, Fuli Feng, Xiangnan He, Shuxian Bi, Zhaolin Ding, Peng Cui
The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning.
1 code implementation • 22 Oct 2020 • Fuli Feng, Weiran Huang, Xiangnan He, Xin Xin, Qifan Wang, Tat-Seng Chua
To this end, we analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction.
2 code implementations • 21 Oct 2020 • Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie
In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.
1 code implementation • 7 Oct 2020 • Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, Xiangnan He
This motivates us to provide a systematic survey of existing work on RS biases.
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.
1 code implementation • 11 Sep 2020 • Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling, Yongdong Zhang
In this paper, we propose a new GCN model named CatGCN, which is tailored for graph learning when the node features are categorical.
1 code implementation • 23 Jun 2020 • Hande Dong, Zhaolin Ding, Xiangnan He, Fuli Feng, Shuxian Bi
In this work, we introduce a new understanding for it -- data augmentation, which is more transparent than the previous understandings.
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.
1 code implementation • 27 May 2020 • Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li, Yongdong Zhang
Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference.
no code implementations • 5 Mar 2020 • Fuli Feng, Xiangnan He, Hanwang Zhang, Tat-Seng Chua
Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data.
1 code implementation • 10 Feb 2020 • Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang
We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
19 code implementations • 20 May 2019 • Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua
Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.
Ranked #6 on Link Prediction on MovieLens 25M
1 code implementation • 20 Feb 2019 • Fuli Feng, Xiangnan He, Jie Tang, Tat-Seng Chua
Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization.
Ranked #3 on Node Classification on NELL
1 code implementation • 23 Nov 2018 • Cunxiao Du, Zhaozheng Chin, Fuli Feng, Lei Zhu, Tian Gan, Liqiang Nie
To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task.
Ranked #4 on Text Classification on Yahoo! Answers
1 code implementation • 13 Oct 2018 • Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, Tat-Seng Chua
The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model.
3 code implementations • 25 Sep 2018 • Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-Seng Chua
Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.
no code implementations • 21 Sep 2018 • Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Lina Yao, Yang song, Depeng Jin
To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.
1 code implementation • 6 May 2018 • Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang
In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.
no code implementations • 17 Apr 2018 • Xuemeng Song, Fuli Feng, Xianjing Han, Xin Yang, Wei Liu, Liqiang Nie
Nevertheless, existing studies overlook the rich valuable knowledge (rules) accumulated in fashion domain, especially the rules regarding clothing matching.