Search Results for author: Tat-Seng Chua

Found 105 papers, 68 papers with code

Re-examining the Role of Schema Linking in Text-to-SQL

no code implementations EMNLP 2020 Wenqiang Lei, Weixin Wang, Zhixin Ma, Tian Gan, Wei Lu, Min-Yen Kan, Tat-Seng Chua

By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking.


Training Free Graph Neural Networks for Graph Matching

1 code implementation14 Jan 2022 Zhiyuan Liu, Yixin Cao, Fuli Feng, Xiang Wang, Xindi Shang, Jie Tang, Kenji Kawaguchi, Tat-Seng Chua

We present TFGM (Training Free Graph Matching), a framework to boost the performance of Graph Neural Networks (GNNs) based graph matching without training.

Deconfounded Training for Graph Neural Networks

no code implementations30 Dec 2021 Yongduo Sui, Xiang Wang, Jiancan Wu, Xiangnan He, Tat-Seng Chua

However, this training paradigm is prone to capture the spurious correlations between the trivial subgraph and the label.

Graph Attention

Video as Conditional Graph Hierarchy for Multi-Granular Question Answering

1 code implementation12 Dec 2021 Junbin Xiao, Angela Yao, Zhiyuan Liu, Yicong Li, Wei Ji, Tat-Seng Chua

To align with the multi-granular essence of linguistic concepts in language queries, we propose to model video as a conditional graph hierarchy which weaves together visual facts of different granularity in a level-wise manner, with the guidance of corresponding textual cues.

Question Answering Video Question Answering

Rethinking the Two-Stage Framework for Grounded Situation Recognition

1 code implementation10 Dec 2021 Meng Wei, Long Chen, Wei Ji, Xiaoyu Yue, Tat-Seng Chua

Since each verb is associated with a specific set of semantic roles, all existing GSR methods resort to a two-stage framework: predicting the verb in the first stage and detecting the semantic roles in the second stage.

Object Recognition

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.

Decoupling Strategy and Surface Realization for Task-oriented Dialogues

no code implementations29 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.

Style Transfer Task-Oriented Dialogue Systems

Why Do We Click: Visual Impression-aware News Recommendation

1 code implementation26 Sep 2021 Jiahao Xun, Shengyu Zhang, Zhou Zhao, Jieming Zhu, Qi Zhang, Jingjie Li, Xiuqiang He, Xiaofei He, Tat-Seng Chua, Fei Wu

In this work, inspired by the fact that users make their click decisions mostly based on the visual impression they perceive when browsing news, we propose to capture such visual impression information with visual-semantic modeling for news recommendation.

Decision Making News Recommendation

CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models

no code implementations24 Sep 2021 Yuan YAO, Ao Zhang, Zhengyan Zhang, Zhiyuan Liu, Tat-Seng Chua, Maosong Sun

Pre-Trained Vision-Language Models (VL-PTMs) have shown promising capabilities in grounding natural language in image data, facilitating a broad variety of cross-modal tasks.

Visual Grounding

CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation

no code implementations11 Sep 2021 Shengyu Zhang, Dong Yao, Zhou Zhao, Tat-Seng Chua, Fei Wu

In this paper, we propose to learn accurate and robust user representations, which are required to be less sensitive to (attack on) noisy behaviors and trust more on the indispensable ones, by modeling counterfactual data distribution.

Representation Learning Sequential Recommendation

How Knowledge Graph and Attention Help? A Qualitative Analysis into Bag-level Relation Extraction

1 code implementation ACL 2021 Zikun Hu, Yixin Cao, Lifu Huang, Tat-Seng Chua

In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE).

Relation Extraction

How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction

1 code implementation26 Jul 2021 Zikun Hu, Yixin Cao, Lifu Huang, Tat-Seng Chua

In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE).

Relation Extraction

NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions

1 code implementation CVPR 2021 Junbin Xiao, Xindi Shang, Angela Yao, Tat-Seng Chua

We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions.

Question Answering Video Question Answering +2

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.

Natural Language Inference Sentiment Analysis

Deconfounded Video Moment Retrieval with Causal Intervention

1 code implementation3 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.

Moment Retrieval

Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting

1 code implementation25 May 2021 Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua, Jinyoung Moon, Hong-Han Shuai

This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020.

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

NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions

1 code implementation18 May 2021 Junbin Xiao, Xindi Shang, Angela Yao, Tat-Seng Chua

We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions.

Question Answering Video Question Answering +2

TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

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.

Question Answering

Leveraging Two Types of Global Graph for Sequential Fashion Recommendation

no code implementations17 May 2021 Yujuan Ding, Yunshan Ma, Wai Keung Wong, Tat-Seng Chua

Sequential fashion recommendation is of great significance in online fashion shopping, which accounts for an increasing portion of either fashion retailing or online e-commerce.

Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media

no code implementations7 May 2021 Yujuan Ding, Yunshan Ma, Lizi Liao, Wai Keung Wong, Tat-Seng Chua

Towards insightful fashion trend forecasting, previous work [1] proposed to analyze more fine-grained fashion elements which can informatively reveal fashion trends.

Time Series

A-FMI: Learning Attributions from Deep Networks via Feature Map Importance

no code implementations12 Apr 2021 An Zhang, Xiang Wang, Chengfang Fang, Jie Shi, Tat-Seng Chua, Zehua Chen

Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs).

Conditional Meta-Network for Blind Super-Resolution with Multiple Degradations

1 code implementation8 Apr 2021 Guanghao Yin, Wei Wang, Zehuan Yuan, Wei Ji, Dongdong Yu, Shouqian Sun, Tat-Seng Chua, Changhu Wang

We extract degradation prior at task-level with the proposed ConditionNet, which will be used to adapt the parameters of the basic SR network (BaseNet).

Image Super-Resolution

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

Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering

no code implementations4 Jan 2021 Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, Tat-Seng Chua

Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents.

Machine Reading Comprehension Open-Domain Question Answering

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.

Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction

1 code implementation27 Nov 2020 Yixin Cao, Jun Kuang, Ming Gao, Aoying Zhou, Yonggang Wen, Tat-Seng Chua

In this paper, we propose a general approach to learn relation prototypesfrom unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient trainingdata.

Relation Extraction Transfer 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 +3

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

Multi-modal Cooking Workflow Construction for Food Recipes

no code implementations20 Aug 2020 Liangming Pan, Jingjing Chen, Jianlong Wu, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Yu-Gang Jiang, Tat-Seng Chua

Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow of the recipe.

Common Sense Reasoning

Neural Sparse Voxel Fields

1 code implementation NeurIPS 2020 Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, Christian Theobalt

We also demonstrate several challenging tasks, including multi-scene learning, free-viewpoint rendering of a moving human, and large-scale scene rendering.

Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval

no code implementations6 Jul 2020 Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, Tat-Seng Chua

To facilitate video retrieval with complex queries, we propose a Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos.

Video Retrieval

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

Few-shot 3D Point Cloud Semantic Segmentation

1 code implementation CVPR 2021 Na Zhao, Tat-Seng Chua, Gim Hee Lee

These fully supervised approaches heavily rely on large amounts of labeled training data that are difficult to obtain and cannot segment new classes after training.

Semantic Segmentation

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

Rethinking Dialogue State Tracking with Reasoning

no code implementations27 May 2020 Lizi Liao, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua

Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management.

Dialogue Management Dialogue State Tracking

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

Knowledge Enhanced Neural Fashion Trend Forecasting

1 code implementation7 May 2020 Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua

Further-more, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Knowledge EnhancedRecurrent Network model (KERN) which takes advantage of the capability of deep recurrent neural networks in modeling time-series data.

Time Series

Semantic Graphs for Generating Deep Questions

1 code implementation ACL 2020 Liangming Pan, Yuxi Xie, Yansong Feng, Tat-Seng Chua, Min-Yen Kan

This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input passage.

Question Generation

Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Zheng Zhang, Lizi Liao, Xiaoyan Zhu, Tat-Seng Chua, Zitao Liu, Yan Huang, Minlie Huang

Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treat the opposite agent policy as part of the environment.

Decision Making

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.

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

Multi-source Domain Adaptation for Visual Sentiment Classification

no code implementations12 Jan 2020 Chuang Lin, Sicheng Zhao, Lei Meng, Tat-Seng Chua

Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data.

Domain Adaptation General Classification +1

SESS: Self-Ensembling Semi-Supervised 3D Object Detection

1 code implementation CVPR 2020 Na Zhao, Tat-Seng Chua, Gim Hee Lee

The performance of existing point cloud-based 3D object detection methods heavily relies on large-scale high-quality 3D annotations.

3D Object Detection Semi-Supervised Image Classification

Heuristic Black-box Adversarial Attacks on Video Recognition Models

1 code implementation21 Nov 2019 Zhipeng Wei, Jingjing Chen, Xingxing Wei, Linxi Jiang, Tat-Seng Chua, Fengfeng Zhou, Yu-Gang Jiang

To overcome this challenge, we propose a heuristic black-box attack model that generates adversarial perturbations only on the selected frames and regions.

Adversarial Attack Video Recognition

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).

Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model

1 code implementation IJCNLP 2019 Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua

Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities.

Entity Alignment Graph Attention +1

Meta-Transfer Learning through Hard Tasks

1 code implementation7 Oct 2019 Qianru Sun, Yaoyao Liu, Zhaozheng Chen, Tat-Seng Chua, Bernt Schiele

In this paper, we propose a novel approach called meta-transfer learning (MTL) which learns to transfer the weights of a deep NN for few-shot learning tasks.

Few-Shot Learning Transfer Learning

Low-Resource Name Tagging Learned with Weakly Labeled Data

1 code implementation IJCNLP 2019 Yixin Cao, Zikun Hu, Tat-Seng Chua, Zhiyuan Liu, Heng Ji

Name tagging in low-resource languages or domains suffers from inadequate training data.


PS^2-Net: A Locally and Globally Aware Network for Point-Based Semantic Segmentation

1 code implementation15 Aug 2019 Na Zhao, Tat-Seng Chua, Gim Hee Lee

In this paper, we present the PS^2-Net -- a locally and globally aware deep learning framework for semantic segmentation on 3D scene-level point clouds.

Scene Segmentation

Automatic Fashion Knowledge Extraction from Social Media

no code implementations12 Aug 2019 Yunshan Ma, Lizi Liao, Tat-Seng Chua

Fashion knowledge plays a pivotal role in helping people in their dressing.

Who, Where, and What to Wear? Extracting Fashion Knowledge from Social Media

no code implementations12 Aug 2019 Yunshan Ma, Xun Yang, Lizi Liao, Yixin Cao, Tat-Seng Chua

We unify three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata.

Human Detection

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

Deep Conversational Recommender in Travel

no code implementations25 Jun 2019 Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua

When traveling to a foreign country, we are often in dire need of an intelligent conversational agent to provide instant and informative responses to our various queries.

Graph Convolutional Network

Learning to Self-Train for Semi-Supervised Few-Shot Classification

1 code implementation NeurIPS 2019 Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele

On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning.

General Classification Meta-Learning

Recent Advances in Neural Question Generation

no code implementations22 May 2019 Liangming Pan, Wenqiang Lei, Tat-Seng Chua, Min-Yen Kan

Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition.

Question Generation

Neural Graph Collaborative Filtering

14 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

5 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

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 Convolutional Network +1

Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation

1 code implementation24 Dec 2018 Kyaw Zaw Lin, Weipeng Xu, Qianru Sun, Christian Theobalt, Tat-Seng Chua

We propose a novel approach to jointly perform 3D shape retrieval and pose estimation from monocular images. In order to make the method robust to real-world image variations, e. g. complex textures and backgrounds, we learn an embedding space from 3D data that only includes the relevant information, namely the shape and pose.

3D Object Retrieval 3D Shape Classification +2

Meta-Transfer Learning for Few-Shot Learning

2 code implementations CVPR 2019 Qianru Sun, Yaoyao Liu, Tat-Seng Chua, Bernt Schiele

In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks.

Few-Shot Image Classification Transfer Learning

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.

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

Temporally Grounding Natural Sentence in Video

no code implementations EMNLP 2018 Jingyuan Chen, Xinpeng Chen, Lin Ma, Zequn Jie, Tat-Seng Chua

We introduce an effective and efficient method that grounds (i. e., localizes) natural sentences in long, untrimmed video sequences.

Video Captioning

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

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

Neural Collaborative Filtering

37 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.

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.

Laplacian-Steered Neural Style Transfer

3 code implementations5 Jul 2017 Shaohua Li, Xinxing Xu, Liqiang Nie, Tat-Seng Chua

However in the traditional optimization objective, low-level features of the content image are absent, and the low-level features of the style image dominate the low-level detail structures of the new image.

Image Generation Style Transfer

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

SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning

1 code implementation CVPR 2017 Long Chen, Hanwang Zhang, Jun Xiao, Liqiang Nie, Jian Shao, Wei Liu, Tat-Seng Chua

Existing visual attention models are generally spatial, i. e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image.

Image Captioning

Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)

1 code implementation9 Jun 2016 Shaohua Li, Tat-Seng Chua, Jun Zhu, Chunyan Miao

Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window.

Document Classification Variational Inference

Online Collaborative Learning for Open-Vocabulary Visual Classifiers

no code implementations CVPR 2016 Hanwang Zhang, Xindi Shang, Wenzhuo Yang, Huan Xu, Huanbo Luan, Tat-Seng Chua

Leveraging on the structure of the proposed collaborative learning formulation, we develop an efficient online algorithm that can jointly learn the label embeddings and visual classifiers.

DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks

1 code implementation5 Jan 2016 Jie Fu, Hongyin Luo, Jiashi Feng, Kian Hsiang Low, Tat-Seng Chua

The performance of deep neural networks is well-known to be sensitive to the setting of their hyperparameters.

Learning Image and User Features for Recommendation in Social Networks

no code implementations ICCV 2015 Xue Geng, Hanwang Zhang, Jingwen Bian, Tat-Seng Chua

It is often a great challenge for traditional recommender systems to learn representative features of both users and images in large social networks, in particular, social curation networks, which are characterized as the extremely sparse links between users and images, and the extremely diverse visual contents of images.

Recommendation Systems

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