Search Results for author: Xiangnan He

Found 96 papers, 65 papers with code

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.

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.

Graph Attention Knowledge Graph Completion +1

Causal Incremental Graph Convolution for Recommender System Retraining

no code implementations16 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

no code implementations5 Aug 2021 Yuyue Zhao, Xiang Wang, Jiawei Chen, Wei Tang, Yashen Wang, 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.

Relation Extraction

Exploring Lottery Ticket Hypothesis in Media Recommender Systems

no code implementations2 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.

Fine-tuning Recommendation Systems +1

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

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

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

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 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 Language understanding +1

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.

Classification General Classification +4

Learning Intents behind Interactions with Knowledge Graph for Recommendation

1 code implementation14 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

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.

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 Inference Multi-Task Learning +1

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 Representation Learning

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.

Causal Inference Graph Attention +2

Self-supervised Graph Learning for Recommendation

1 code implementation21 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.

Graph Learning Representation Learning +1

Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue

no code implementations21 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 Inference

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.

Recommendation Systems

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

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.

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

2 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

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

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

Denoising Recommendation Systems

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.

Hierarchical structure

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

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

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.

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

1 code implementation10 Mar 2020 Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, Zibin Zheng

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

Graph Clustering Link Prediction +1

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 Recommendation Systems

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 Node Classification

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

9 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 Recommendation Systems

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.

Fine-tuning Recommendation Systems +1

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).

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 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 Session-Based Recommendations

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

Neural Graph Collaborative Filtering

13 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

KGAT: Knowledge Graph Attention Network for Recommendation

6 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.

Graph Attention Knowledge Graphs +2

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

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 Node Classification

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.

Graph Generation Scene Graph Generation +1

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

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.

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

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 Outlier Detection

Temporal Relational Ranking for Stock Prediction

2 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.

Stock Prediction Time Series

Learning to Recommend with Multiple Cascading Behaviors

no code implementations21 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.

Multi-Task Learning Recommendation Systems

Adversarial Training Towards Robust Multimedia Recommender System

1 code implementation19 Sep 2018 Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, Tat-Seng Chua

To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning.

Information Retrieval Multimedia

Aesthetic-based Clothing Recommendation

no code implementations16 Sep 2018 Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong, Zheng Qin

Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner.

Recommendation Systems

A Simple Convolutional Generative Network for Next Item Recommendation

2 code implementations15 Aug 2018 Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, Xiangnan He

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.

Recommendation Systems

Outer Product-based Neural Collaborative Filtering

1 code implementation12 Aug 2018 Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering.

Collaborative Filtering

Adversarial Personalized Ranking for Recommendation

1 code implementation12 Aug 2018 Xiangnan He, Zhankui He, Xiaoyu Du, Tat-Seng Chua

Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it outperforms BPR with a relative improvement of 11. 2% on average and achieves state-of-the-art performance for item recommendation.

Recommendation Systems

Discrete Factorization Machines for Fast Feature-based Recommendation

1 code implementation6 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.

Binarization Quantization

Neural Collaborative Filtering

36 code implementations WWW 2017 Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua

When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.

Collaborative Filtering Recommendation Systems +1

Neural Factorization Machines for Sparse Predictive Analytics

4 code implementations16 Aug 2017 Xiangnan He, Tat-Seng Chua

However, FM models feature interactions in a linear way, which can be insufficient for capturing the non-linear and complex inherent structure of real-world data.

Link Prediction

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

3 code implementations16 Aug 2017 Xiangnan He, Hanwang Zhang, Min-Yen Kan, Tat-Seng Chua

To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data.

BiRank: Towards Ranking on Bipartite Graphs

3 code implementations15 Aug 2017 Xiangnan He, Ming Gao, Min-Yen Kan, Dingxian Wang

In this paper, we study the problem of ranking vertices of a bipartite graph, based on the graph's link structure as well as prior information about vertices (which we term a query vector).

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

4 code implementations15 Aug 2017 Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, Tat-Seng Chua

Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions.

Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

no code implementations13 Jul 2017 Lei Zhu, Zi Huang, Xiaobai Liu, Xiangnan He, Jingkuan Song, Xiaofang Zhou

Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises.

Item Silk Road: Recommending Items from Information Domains to Social Users

no code implementations10 Jun 2017 Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua

In this work, we address the problem of cross-domain social recommendation, i. e., recommending relevant items of information domains to potential users of social networks.

Collaborative Ranking Recommendation Systems

Attributed Social Network Embedding

1 code implementation14 May 2017 Lizi Liao, Xiangnan He, Hanwang Zhang, Tat-Seng Chua

For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks.

Social and Information Networks

A Generic Coordinate Descent Framework for Learning from Implicit Feedback

no code implementations15 Nov 2016 Immanuel Bayer, Xiangnan He, Bhargav Kanagal, Steffen Rendle

A diversity of complex models has been proposed for a wide variety of applications.

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