Search Results for author: Xiangnan He

Found 173 papers, 119 papers with code

Text-like Encoding of Collaborative Information in Large Language Models for Recommendation

no code implementations5 Jun 2024 Yang Zhang, Keqin Bao, Ming Yan, Wenjie Wang, Fuli Feng, Xiangnan He

BinLLM converts collaborative embeddings from external models into binary sequences -- a specific text format that LLMs can understand and operate on directly, facilitating the direct usage of collaborative information in text-like format by LLMs.

Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach

1 code implementation2 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.

Attribute Fairness +1

Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference

1 code implementation30 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.

Causal Inference Recommendation Systems +1

A Survey of Generative Search and Recommendation in the Era of Large Language Models

no code implementations25 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.

Exact and Efficient Unlearning for Large Language Model-based Recommendation

no code implementations16 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.

Language Modelling Large Language Model

Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model

1 code implementation CVPR 2024 Zhicai Wang, Longhui Wei, Tan Wang, Heyu Chen, Yanbin Hao, Xiang Wang, Xiangnan He, Qi Tian

Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications.

Data Augmentation Image Classification

Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients

no code implementations26 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.

Fairness Recommendation Systems

Boosting Few-Shot Learning via Attentive Feature Regularization

no code implementations23 Mar 2024 Xingyu Zhu, Shuo Wang, Jinda Lu, Yanbin Hao, Haifeng Liu, Xiangnan He

Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from different categories with a blending factor.

Few-Shot Learning

Proactive Recommendation with Iterative Preference Guidance

no code implementations12 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.

Recommendation Systems

The 2nd Workshop on Recommendation with Generative Models

no code implementations7 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.

Recommendation Systems

Large Language Models are Learnable Planners for Long-Term Recommendation

1 code implementation29 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.

Decision Making Language Modelling +2

Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation

no code implementations29 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.

Recommendation Systems

Diffusion Models for Generative Outfit Recommendation

1 code implementation27 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.

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

1 code implementation23 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

Towards 3D Molecule-Text Interpretation in Language Models

1 code implementation25 Jan 2024 Sihang Li, Zhiyuan Liu, Yanchen Luo, Xiang Wang, Xiangnan He, Kenji Kawaguchi, Tat-Seng Chua, Qi Tian

Through 3D molecule-text alignment and 3D molecule-centric instruction tuning, 3D-MoLM establishes an integration of 3D molecular encoder and LM.

Instruction Following Language Modelling +3

Alleviating Structural Distribution Shift in Graph Anomaly Detection

1 code implementation25 Jan 2024 Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang

Graph anomaly detection (GAD) is a challenging binary classification problem due to its different structural distribution between anomalies and normal nodes -- abnormal nodes are a minority, therefore holding high heterophily and low homophily compared to normal nodes.

Binary Classification Graph Anomaly Detection

Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems

no code implementations25 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.

Incremental Learning Language Modelling +2

CAR: Consolidation, Augmentation and Regulation for Recipe Retrieval

no code implementations8 Dec 2023 Fangzhou Song, Bin Zhu, Yanbin Hao, Shuo Wang, Xiangnan He

Learning recipe and food image representation in common embedding space is non-trivial but crucial for cross-modal recipe retrieval.

Retrieval

LLaRA: Large Language-Recommendation Assistant

1 code implementation5 Dec 2023 Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, Xiangnan He

Treating the "sequential behaviors of users" as a distinct modality beyond texts, we employ a projector to align the traditional recommender's ID embeddings with the LLM's input space.

Language Modelling Sequential Recommendation +1

Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion

1 code implementation NeurIPS 2023 Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang, Yancheng Yuan, Xiangnan He

Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence.

Denoising Sequential Recommendation

Large Language Model Can Interpret Latent Space of Sequential Recommender

2 code implementations31 Oct 2023 Zhengyi Yang, Jiancan Wu, Yanchen Luo, Jizhi Zhang, Yancheng Yuan, An Zhang, Xiang Wang, Xiangnan He

Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items.

Language Modelling Large Language Model +1

CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation

1 code implementation30 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.

Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation

no code implementations25 Oct 2023 Chengpeng Li, Zhengyi Yang, Jizhi Zhang, Jiancan Wu, Dingxian Wang, Xiangnan He, Xiang Wang

Therefore, the data sparsity issue of reward signals and state transitions is very severe, while it has long been overlooked by existing RL recommenders. Worse still, RL methods learn through the trial-and-error mode, but negative feedback cannot be obtained in implicit feedback recommendation tasks, which aggravates the overestimation problem of offline RL recommender.

Contrastive Learning Offline RL +3

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 Content-Driven Micro-Video Recommendation Dataset at Scale

1 code implementation27 Sep 2023 Yongxin Ni, Yu Cheng, Xiangyan Liu, Junchen Fu, Youhua Li, Xiangnan He, Yongfeng Zhang, Fajie Yuan

Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and e-commerce industries.

Benchmarking Recommendation Systems +1

Text-to-Image Generation for Abstract Concepts

no code implementations26 Sep 2023 Jiayi Liao, Xu Chen, Qiang Fu, Lun Du, Xiangnan He, Xiang Wang, Shi Han, Dongmei Zhang

Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts.

Text-to-Image Generation

Selective Volume Mixup for Video Action Recognition

no code implementations18 Sep 2023 Yi Tan, Zhaofan Qiu, Yanbin Hao, Ting Yao, Xiangnan He, Tao Mei

In this paper, we propose a novel video augmentation strategy named Selective Volume Mixup (SV-Mix) to improve the generalization ability of deep models with limited training videos.

Action Recognition Image Augmentation +1

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

CgT-GAN: CLIP-guided Text GAN for Image Captioning

1 code implementation23 Aug 2023 Jiarui Yu, Haoran Li, Yanbin Hao, Bin Zhu, Tong Xu, Xiangnan He

Particularly, we use adversarial training to teach CgT-GAN to mimic the phrases of an external text corpus and CLIP-based reward to provide semantic guidance.

Image Captioning

Recommendation Unlearning via Influence Function

no code implementations5 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.

How Graph Convolutions Amplify Popularity Bias for Recommendation?

1 code implementation24 May 2023 Jiajia Chen, Jiancan Wu, Jiawei Chen, Xin Xin, Yong Li, Xiangnan He

Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (\textit{i. e.,} neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential.

Recommendation Systems

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

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

GIF: A General Graph Unlearning Strategy via Influence Function

1 code implementation6 Apr 2023 Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, Xiangnan He

Then, we recognize the crux to the inability of traditional influence function for graph unlearning, and devise Graph Influence Function (GIF), a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $\epsilon$-mass perturbation in deleted data.

Machine Unlearning

Bi-directional Distribution Alignment for Transductive Zero-Shot Learning

1 code implementation CVPR 2023 Zhicai Wang, Yanbin Hao, Tingting Mu, Ouxiang Li, Shuo Wang, Xiangnan He

It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match.

Zero-Shot Learning

Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors

no code implementations17 Feb 2023 Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie

In this paper, we propose Fine-Grained Translation Error Detection (FG-TED) task, aiming at identifying both the position and the type of translation errors on given source-hypothesis sentence pairs.

Position Sentence +1

Adap-$τ$: Adaptively Modulating Embedding Magnitude for Recommendation

2 code implementations9 Feb 2023 Jiawei Chen, Junkang Wu, Jiancan Wu, Sheng Zhou, Xuezhi Cao, Xiangnan He

Recent years have witnessed the great successes of embedding-based methods in recommender systems.

Recommendation Systems

Weakly Supervised Anomaly Detection: A Survey

2 code implementations9 Feb 2023 Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S. Yu, Yue Zhao

Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.

Supervised Anomaly Detection Time Series +2

FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion

no code implementations7 Feb 2023 Wentao Shi, Junkang Wu, Xuezhi Cao, Jiawei Chen, Wenqiang Lei, Wei Wu, Xiangnan He

Specifically, they suffer from two main limitations: 1) existing Graph Convolutional Network (GCN) methods in hyperbolic space rely on tangent space approximation, which would incur approximation error in representation learning, and 2) due to the lack of inner product operation definition in hyperbolic space, existing methods can only measure the plausibility of facts (links) with hyperbolic distance, which is difficult to capture complex data patterns.

Knowledge Graph Completion Representation Learning

On the Theories Behind Hard Negative Sampling for Recommendation

1 code implementation7 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.

Recommendation Systems

Unbiased Knowledge Distillation for Recommendation

1 code implementation27 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.

Knowledge Distillation Model Compression +1

Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

1 code implementation NeurIPS 2023 Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He

From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstable and can lead to the two primary types of distribution shifts.

Data Augmentation Graph Classification +2

Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task

1 code implementation18 Oct 2022 Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie

In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation).

Sentence XLM-R

Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation

1 code implementation10 Oct 2022 Kang Liu, Feng Xue, Xiangnan He, Dan Guo, Richang Hong

In this work, we propose to model multi-grained popularity features and jointly learn them together with high-order connectivity, to match the differentiation of user preferences exhibited in popularity features.

Collaborative Filtering Recommendation Systems

Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation

1 code implementation22 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.

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

Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP

1 code implementation15 Jul 2022 Zhicai Wang, Yanbin Hao, Xingyu Gao, Hao Zhang, Shuo Wang, Tingting Mu, Xiangnan He

They use token-mixing layers to capture cross-token interactions, as opposed to the multi-head self-attention mechanism used by Transformers.

LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization

no code implementations23 Jun 2022 Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, Yidong Li

Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages.

Privacy Preserving Recommendation Systems

Let Invariant Rationale Discovery Inspire Graph Contrastive Learning

1 code implementation16 Jun 2022 Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua

Specifically, without supervision signals, RGCL uses a rationale generator to reveal salient features about graph instance-discrimination as the rationale, and then creates rationale-aware views for contrastive learning.

Contrastive Learning

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

no code implementations13 Jun 2022 Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M. Jose, Chenyun Yu, Beibei Kong, Xiangnan He, Zhijin Wang, Bo Hu, Zang Li

That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.

Recommendation Systems Transfer Learning

Differentiable Invariant Causal Discovery

no code implementations31 May 2022 Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan, Xiangnan He, Tat-Seng Chua

This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.

Causal Discovery

Mitigating Hidden Confounding Effects for Causal Recommendation

no code implementations16 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.

Multi-Task Learning Recommendation Systems

Addressing Confounding Feature Issue for Causal Recommendation

1 code implementation13 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.

Recommendation Systems

Cross Pairwise Ranking for Unbiased Item Recommendation

1 code implementation26 Apr 2022 Qi Wan, Xiangnan He, Xiang Wang, Jiancan Wu, Wei Guo, Ruiming Tang

In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism.

Recommendation Systems

Reinforced Causal Explainer for Graph Neural Networks

1 code implementation23 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.

Graph Classification

Attention in Attention: Modeling Context Correlation for Efficient Video Classification

1 code implementation20 Apr 2022 Yanbin Hao, Shuo Wang, Pei Cao, Xinjian Gao, Tong Xu, Jinmeng Wu, Xiangnan He

Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts.

Video Classification

Rumor Detection with Self-supervised Learning on Texts and Social Graph

no code implementations19 Apr 2022 Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, Yongdong Zhang

At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth.

Self-Supervised Learning

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

1 code implementation4 Apr 2022 Chongming Gao, Shiqi Wang, Shijun Li, Jiawei Chen, Xiangnan He, Wenqiang Lei, Biao Li, Yuan Zhang, Peng Jiang

The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction.

Causal Inference counterfactual +2

Group Contextualization for Video Recognition

1 code implementation CVPR 2022 Yanbin Hao, Hao Zhang, Chong-Wah Ngo, Xiangnan He

By utilizing calibrators to embed feature with four different kinds of contexts in parallel, the learnt representation is expected to be more resilient to diverse types of activities.

Action Recognition Egocentric Activity Recognition +1

KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

3 code implementations22 Feb 2022 Chongming Gao, Shijun Li, Wenqiang Lei, Jiawei Chen, Biao Li, Peng Jiang, Xiangnan He, Jiaxin Mao, Tat-Seng Chua

The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive.

Recommendation Systems User Simulation

IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search

1 code implementation10 Feb 2022 Dian Cheng, Jiawei Chen, Wenjun Peng, Wenqin Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, Xiangnan He

On this basis, we develop a specific interactive hypergraph neural network to explicitly encode the structure information (i. e., collaborative signal) into the embedding process.

Representation Learning

Discovering Invariant Rationales for Graph Neural Networks

1 code implementation ICLR 2022 Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, Tat-Seng Chua

Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction.

Graph Classification

Deconfounding to Explanation Evaluation in Graph Neural Networks

no code implementations21 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.

Causal Attention for Interpretable and Generalizable Graph Classification

1 code implementation30 Dec 2021 Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, Tat-Seng Chua

To endow the classifier with better interpretation and generalization, we propose the Causal Attention Learning (CAL) strategy, which discovers the causal patterns and mitigates the confounding effect of shortcuts.

Graph Attention Graph Classification

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

Towards Multi-Grained Explainability for Graph Neural Networks

1 code implementation NeurIPS 2021 Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua

A performant paradigm towards multi-grained explainability is until-now lacking and thus a focus of our work.

Graph Neural Network

Inductive Lottery Ticket Learning for Graph Neural Networks

no code implementations29 Sep 2021 Yongduo Sui, Xiang Wang, Tianlong Chen, Xiangnan He, Tat-Seng Chua

In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity.

Graph Classification Node Classification +1

Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation

no code implementations16 Sep 2021 Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He, Xuezhi Cao, Fuzheng Zhang, Wei Wu

To sufficiently exploit such important information for recommendation, it is essential to disentangle the benign popularity bias caused by item quality from the harmful popularity bias caused by conformity.

Recommendation Systems

DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network

2 code implementations22 Aug 2021 Junkang Wu, Wentao Shi, Xuezhi Cao, Jiawei Chen, Wenqiang Lei, Fuzheng Zhang, Wei Wu, Xiangnan He

Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks.

Disentanglement Graph Attention +1

Causal Incremental Graph Convolution for Recommender System Retraining

1 code implementation16 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.

Causal Inference Recommendation Systems

Time-aware Path Reasoning on Knowledge Graph for Recommendation

1 code implementation5 Aug 2021 Yuyue Zhao, Xiang Wang, Jiawei Chen, Yashen Wang, Wei Tang, Xiangnan He, Haiyong Xie

In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations.

Explainable Recommendation Relation Extraction

Exploring Lottery Ticket Hypothesis in Media Recommender Systems

1 code implementation2 Aug 2021 Yanfang Wang, Yongduo Sui, Xiang Wang, Zhenguang Liu, Xiangnan He

We get inspirations from the recently proposed lottery ticket hypothesis (LTH), which argues that the dense and over-parameterized model contains a much smaller and sparser sub-model that can reach comparable performance to the full model.

Recommendation Systems Representation Learning

User-specific Adaptive Fine-tuning for Cross-domain Recommendations

no code implementations15 Jun 2021 Lei Chen, Fajie Yuan, Jiaxi Yang, Xiangnan He, Chengming Li, Min Yang

Fine-tuning works as an effective transfer learning technique for this objective, which adapts the parameters of a pre-trained model from the source domain to the target domain.

Recommendation Systems Transfer Learning

Empowering Language Understanding with Counterfactual Reasoning

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.

counterfactual Counterfactual Reasoning +2

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

Learning Robust Recommenders through Cross-Model Agreement

no code implementations20 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.

Denoising Recommendation Systems

Graph Learning based Recommender Systems: A Review

1 code implementation13 May 2021 Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).

Collaborative Filtering Graph Learning +1

Causal Intervention for Leveraging Popularity Bias in Recommendation

1 code implementation13 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.

Collaborative Filtering Recommendation Systems

AutoDebias: Learning to Debias for Recommendation

1 code implementation10 May 2021 Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, Keping Yang

This provides a valuable opportunity to develop a universal solution for debiasing, e. g., by learning the debiasing parameters from data.

Imputation Meta-Learning +1

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

1 code implementation27 Apr 2021 Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.

Collaborative Filtering Sequential Recommendation

Structure-Enhanced Meta-Learning For Few-Shot Graph Classification

1 code implementation5 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.

General Classification Graph Classification +4

Learning Intents behind Interactions with Knowledge Graph for Recommendation

2 code implementations14 Feb 2021 Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, Tat-Seng Chua

In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN).

Recommendation Systems Relation

Causal Screening to Interpret Graph Neural Networks

no code implementations1 Jan 2021 Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua

In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect.

Explanation Generation

On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner

1 code implementation29 Nov 2020 Na Li, Renyu Zhu, Xiaoxu Zhou, Xiangnan He, Wenyuan Cai, Ming Gao, Aoying Zhou

In this paper, we model the author disambiguation as a collaboration network reconstruction problem, and propose an incremental and unsupervised author disambiguation method, namely IUAD, which performs in a bottom-up manner.

CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation

no code implementations16 Nov 2020 Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan He

To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.

Recommendation Systems

Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

1 code implementation29 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.

counterfactual Counterfactual Inference +3

On the Equivalence of Decoupled Graph Convolution Network and Label Propagation

1 code implementation23 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.

Node Classification Pseudo Label +1

Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method

1 code implementation22 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.

Blocking Causal Inference +4

Self-supervised Graph Learning for Recommendation

2 code implementations21 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.

Collaborative Filtering Graph Learning +2

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

CatGCN: Graph Convolutional Networks with Categorical Node Features

1 code implementation11 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.

Graph Learning Node Classification +1

Adversarial Attack on Large Scale Graph

1 code implementation8 Sep 2020 Jintang Li, Tao Xie, Liang Chen, Fenfang Xie, Xiangnan He, Zibin Zheng

Currently, most works on attacking GNNs are mainly using gradient information to guide the attack and achieve outstanding performance.

Adversarial Attack

A Survey on Large-scale Machine Learning

1 code implementation10 Aug 2020 Meng Wang, Weijie Fu, Xiangnan He, Shijie Hao, Xindong Wu

Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems.

BIG-bench Machine Learning Computational Efficiency +1

Disentangled Graph Collaborative Filtering

2 code implementations3 Jul 2020 Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua

Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations.

Collaborative Filtering Disentanglement

Interactive Path Reasoning on Graph for Conversational Recommendation

no code implementations1 Jul 2020 Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, Tat-Seng Chua

Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference.

Attribute Recommendation Systems

Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning

1 code implementation23 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.

Data Augmentation Graph Learning

Disentangling User Interest and Conformity for Recommendation with Causal Embedding

3 code implementations19 Jun 2020 Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Depeng Jin, Yong Li

We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.

Causal Inference

Denoising Implicit Feedback for Recommendation

2 code implementations7 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

Modeling Personalized Item Frequency Information for Next-basket Recommendation

2 code implementations31 May 2020 Haoji Hu, Xiangnan He, Jinyang Gao, Zhi-Li Zhang

NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items.

Next-basket recommendation Session-Based Recommendations

How to Retrain Recommender System? A Sequential Meta-Learning Method

1 code implementation27 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.

Meta-Learning Recommendation Systems

Hierarchical Fashion Graph Network for Personalized Outfit Recommendation

1 code implementation26 May 2020 Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua

Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities. Distinct from other scenarios (e. g., social networking or content sharing) which recommend a single item (e. g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items. Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference.

Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users

1 code implementation23 May 2020 Shijun Li, Wenqiang Lei, Qingyun Wu, Xiangnan He, Peng Jiang, Tat-Seng Chua

In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively.

Collaborative Filtering Thompson Sampling

Bundle Recommendation with Graph Convolutional Networks

1 code implementation7 May 2020 Jianxin Chang, Chen Gao, Xiangnan He, Yong Li, Depeng Jin

Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles.

Decision Making Graph Neural Network

Modelling High-Order Social Relations for Item Recommendation

no code implementations23 Mar 2020 Yang Liu, Liang Chen, Xiangnan He, Jiaying Peng, Zibin Zheng, Jie Tang

The prevalence of online social network makes it compulsory to study how social relations affect user choice.

Vocal Bursts Intensity Prediction

Reinforced Negative Sampling over Knowledge Graph for Recommendation

1 code implementation12 Mar 2020 Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua

Properly handling missing data is a fundamental challenge in recommendation.

A Survey of Adversarial Learning on Graphs

2 code implementations10 Mar 2020 Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, Zibin Zheng, Bingzhe Wu

To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.

Clustering Graph Clustering +2

Price-aware Recommendation with Graph Convolutional Networks

1 code implementation9 Mar 2020 Yu Zheng, Chen Gao, Xiangnan He, Yong Li, Depeng Jin

Price, an important factor in marketing --- which determines whether a user will make the final purchase decision on an item --- surprisingly, has received relatively little scrutiny.

Collaborative Filtering Marketing +1

Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions

no code implementations5 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.

Document Classification

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems

no code implementations21 Feb 2020 Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat-Seng Chua

Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models.

Recommendation Systems

Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network

no code implementations20 Feb 2020 Yuanyuan Jin, Wei zhang, Xiangnan He, Xinyu Wang, Xiaoling Wang

Given a set of symptoms to treat, we aim to generate an overall syndrome representation by effectively fusing the embeddings of all the symptoms in the set, to mimic how a doctor induces the syndromes.

Bilinear Graph Neural Network with Neighbor Interactions

1 code implementation10 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.

General Classification Graph Neural Network +1

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

17 code implementations6 Feb 2020 Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang

We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.

Collaborative Filtering Graph Classification +1

Graph Convolution Machine for Context-aware Recommender System

1 code implementation30 Jan 2020 Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, Xing Xie

The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph.

Collaborative Filtering Decoder +1

Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation

1 code implementation13 Jan 2020 Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, Liguang Zhang

To overcome this issue, we develop a parameter efficient transfer learning architecture, termed as PeterRec, which can be configured on-the-fly to various downstream tasks.

Recommendation Systems Transfer Learning

Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction

1 code implementation17 Nov 2019 Haozhe Wu, Zhiyuan Hu, Jia Jia, Yaohua Bu, Xiangnan He, Tat-Seng Chua

Next, we define user's attributes as two categories: spatial attributes (e. g., social role of user) and temporal attributes (e. g., post content of user).

Informativeness

MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video

1 code implementation ACM International Conference on Multimedia 2019 Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, Tat-Seng Chua

Existing works on multimedia recommendation largely exploit multi-modal contents to enrich item representations, while less effort is made to leverage information interchange between users and items to enhance user representations and further capture user's fine-grained preferences on different modalities.

Microvideo Recommendation Micro-video recommendations +4

Improving Neural Relation Extraction with Implicit Mutual Relations

1 code implementation8 Jul 2019 Jun Kuang, Yixin Cao, Jianbing Zheng, Xiangnan He, Ming Gao, Aoying Zhou

In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible.

Relation Relation Extraction

Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering

1 code implementation26 Jun 2019 Xiaoyu Du, Xiangnan He, Fajie Yuan, Jinhui Tang, Zhiguang Qin, Tat-Seng Chua

In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF.

Collaborative Filtering Recommendation Systems

Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

no code implementations11 Jun 2019 Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, Yilin Xiong

To capture the sequential dependencies, existing methods resort either to data augmentation techniques or left-to-right style autoregressive training. Since these methods are aimed to model the sequential nature of user behaviors, they ignore the future data of a target interaction when constructing the prediction model for it.

Data Augmentation Decoder +2

Learning to Compose and Reason with Language Tree Structures for Visual Grounding

no code implementations5 Jun 2019 Richang Hong, Daqing Liu, Xiaoyu Mo, Xiangnan He, Hanwang Zhang

Grounding natural language in images, such as localizing "the black dog on the left of the tree", is one of the core problems in artificial intelligence, as it needs to comprehend the fine-grained and compositional language space.

Visual Grounding Visual Reasoning

LambdaOpt: Learn to Regularize Recommender Models in Finer Levels

1 code implementation28 May 2019 Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, Yue Wang

We show how to employ LambdaOpt on matrix factorization, a classical model that is representative of a large family of recommender models.

Hyperparameter Optimization Recommendation Systems

KGAT: Knowledge Graph Attention Network for Recommendation

7 code implementations20 May 2019 Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.

Explainable Recommendation Graph Neural Network +2

Neural Graph Collaborative Filtering

20 code implementations20 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.

Collaborative Filtering Link Prediction +1

Visually-aware Recommendation with Aesthetic Features

no code implementations2 May 2019 Wenhui Yu, Xiangnan He, Jian Pei, Xu Chen, Li Xiong, Jinfei Liu, Zheng Qin

While recent developments on visually-aware recommender systems have taken the product image into account, none of them has considered the aesthetic aspect.

Decision Making Recommendation Systems +1

Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

2 code implementations29 Apr 2019 Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, Joemon Jose

In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system.

Collaborative Filtering Recommendation Systems +1

Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure

1 code implementation20 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.

General Classification Graph Neural Network +1

Learning Vertex Representations for Bipartite Networks

1 code implementation16 Jan 2019 Ming Gao, Xiangnan He, Leihui Chen, Tingting Liu, Jinglin Zhang, Aoying Zhou

Recent years have witnessed a widespread increase of interest in network representation learning (NRL).

Collaborative Filtering Knowledge Graphs +2

Counterfactual Critic Multi-Agent Training for Scene Graph Generation

no code implementations ICCV 2019 Long Chen, Hanwang Zhang, Jun Xiao, Xiangnan He, ShiLiang Pu, Shih-Fu Chang

CMAT is a multi-agent policy gradient method that frames objects as cooperative agents, and then directly maximizes a graph-level metric as the reward.

counterfactual Graph Generation +2

Explainable Reasoning over Knowledge Graphs for Recommendation

2 code implementations12 Nov 2018 Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua

Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest.

Knowledge Graphs Recommendation Systems

Fast Matrix Factorization with Non-Uniform Weights on Missing Data

1 code implementation11 Nov 2018 Xiangnan He, Jinhui Tang, Xiaoyu Du, Richang Hong, Tongwei Ren, Tat-Seng Chua

This poses an imbalanced learning problem, since the scale of missing entries is usually much larger than that of observed entries, but they cannot be ignored due to the valuable negative signal.

Deep Item-based Collaborative Filtering for Top-N Recommendation

1 code implementation11 Nov 2018 Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong

In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items.

Collaborative Filtering Decision Making +1

Attentive Aspect Modeling for Review-aware Recommendation

no code implementations11 Nov 2018 Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, Tat-Seng Chua

The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product.

Enhancing Stock Movement Prediction with Adversarial Training

1 code implementation13 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.

Stock Prediction

Generative Adversarial Active Learning for Unsupervised Outlier Detection

2 code implementations28 Sep 2018 Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, Xiangnan He

In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution.

Active Learning Binary Classification +1

Temporal Relational Ranking for Stock Prediction

3 code implementations25 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.

Relation Network Stock Prediction +1