Search Results for author: Hongzhi Yin

Found 139 papers, 44 papers with code

BiVRec: Bidirectional View-based Multimodal Sequential Recommendation

no code implementations27 Feb 2024 Jiaxi Hu, Jingtong Gao, Xiangyu Zhao, Yuehong Hu, Yuxuan Liang, Yiqi Wang, Ming He, Zitao Liu, Hongzhi Yin

The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research.

Semantic Similarity Semantic Textual Similarity +1

Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation

no code implementations31 Jan 2024 Liang Qu, Wei Yuan, Ruiqi Zheng, Lizhen Cui, Yuhui Shi, Hongzhi Yin

To bridge this gap, this paper explores a user-governed data contribution federated recommendation architecture where users are free to take control of whether they share data and the proportion of data they share to the server.

Contrastive Learning Recommendation Systems

OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation

no code implementations29 Jan 2024 Weicong Tan, Weiqing Wang, Xin Zhou, Wray Buntine, Gordon Bingham, Hongzhi Yin

Most existing medication recommendation models learn representations for medical concepts based on electronic health records (EHRs) and make recommendations with learnt representations.

Challenging Low Homophily in Social Recommendation

no code implementations26 Jan 2024 Wei Jiang, Xinyi Gao, Guandong Xu, Tong Chen, Hongzhi Yin

To comprehensively extract preference-aware homophily information latent in the social graph, we propose Social Heterophily-alleviating Rewiring (SHaRe), a data-centric framework for enhancing existing graph-based social recommendation models.

Contrastive Learning

Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation

no code implementations24 Jan 2024 Ruiqi Zheng, Liang Qu, Tong Chen, Lizhen Cui, Yuhui Shi, Hongzhi Yin

Collaborative Learning (CL) emerges to promote model sharing among users, where reference data is an intermediary that allows users to exchange their soft decisions without directly sharing their private data or parameters, ensuring privacy and benefiting from collaboration.

Graph Condensation: A Survey

no code implementations22 Jan 2024 Xinyi Gao, Junliang Yu, Wei Jiang, Tong Chen, Wentao Zhang, Hongzhi Yin

The burgeoning volume of graph data poses significant challenges in storage, transmission, and particularly the training of graph neural networks (GNNs).

Fairness Graph Generation

On-Device Recommender Systems: A Comprehensive Survey

no code implementations21 Jan 2024 Hongzhi Yin, Liang Qu, Tong Chen, Wei Yuan, Ruiqi Zheng, Jing Long, Xin Xia, Yuhui Shi, Chengqi Zhang

Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), which leverage the capabilities of edge devices to minimize centralized data storage requirements, reduce the response latency caused by communication overheads, and enhance user privacy and security by localizing data processing and model training.

Recommendation Systems

ROIC-DM: Robust Text Inference and Classification via Diffusion Model

no code implementations7 Jan 2024 Shilong Yuan, Wei Yuan, Hongzhi Yin, Tieke He

While language models have made many milestones in text inference and classification tasks, they remain susceptible to adversarial attacks that can lead to unforeseen outcomes.

Denoising

Poisoning Attacks against Recommender Systems: A Survey

1 code implementation3 Jan 2024 Zongwei Wang, Min Gao, Junliang Yu, Hao Ma, Hongzhi Yin, Shazia Sadiq

This survey paper provides a systematic and up-to-date review of the research landscape on Poisoning Attacks against Recommendation (PAR).

Recommendation Systems

Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion

no code implementations25 Dec 2023 Lijian Chen, Wei Yuan, Tong Chen, Guanhua Ye, Quoc Viet Hung Nguyen, Hongzhi Yin

Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences.

Recommendation Systems

PUMA: Efficient Continual Graph Learning with Graph Condensation

1 code implementation22 Dec 2023 Yilun Liu, Ruihong Qiu, Yanran Tang, Hongzhi Yin, Zi Huang

Our prior work, CaT is a replay-based framework with a balanced continual learning procedure, which designs a small yet effective memory bank for replaying data by condensing incoming graphs.

Continual Learning Graph Learning +1

On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation Paradigm

no code implementations18 Dec 2023 Hongzhi Yin, Tong Chen, Liang Qu, Bin Cui

Given the sheer volume of contemporary e-commerce applications, recommender systems (RSs) have gained significant attention in both academia and industry.

Recommendation Systems

Hide Your Model: A Parameter Transmission-free Federated Recommender System

1 code implementation25 Nov 2023 Wei Yuan, Chaoqun Yang, Liang Qu, Quoc Viet Hung Nguyen, JianXin Li, Hongzhi Yin

Existing FedRecs generally adhere to a learning protocol in which a central server shares a global recommendation model with clients, and participants achieve collaborative learning by frequently communicating the model's public parameters.

Privacy Preserving Recommendation Systems

Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution

1 code implementation19 Nov 2023 Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin

The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution.

Anomaly Detection Contrastive Learning +3

Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation

no code implementations1 Nov 2023 Jiangnan Xia, Yu Yang, Senzhang Wang, Hongzhi Yin, Jiannong Cao, Philip S. Yu

To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network.

Data Augmentation Representation Learning

Defense Against Model Extraction Attacks on Recommender Systems

1 code implementation25 Oct 2023 Sixiao Zhang, Hongzhi Yin, Hongxu Chen, Cheng Long

These gradients are used to compute a swap loss, which maximizes the loss of the student model.

Model extraction Recommendation Systems

Budgeted Embedding Table For Recommender Systems

no code implementations23 Oct 2023 Yunke Qu, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin

Experiments have shown state-of-the-art performance on two real-world datasets when BET is paired with three popular recommender models under different memory budgets.

Recommendation Systems Representation Learning

Motif-Based Prompt Learning for Universal Cross-Domain Recommendation

no code implementations20 Oct 2023 Bowen Hao, Chaoqun Yang, Lei Guo, Junliang Yu, Hongzhi Yin

By unifying pre-training and recommendation tasks as a common motif-based similarity learning task and integrating adaptable prompt parameters to guide the model in downstream recommendation tasks, MOP excels in transferring domain knowledge effectively.

General Knowledge Multi-Task Learning

Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation

no code implementations17 Oct 2023 Xinyi Gao, Wentao Zhang, Junliang Yu, Yingxia Shao, Quoc Viet Hung Nguyen, Bin Cui, Hongzhi Yin

To further accelerate Scalable GNNs inference in this inductive setting, we propose an online propagation framework and two novel node-adaptive propagation methods that can customize the optimal propagation depth for each node based on its topological information and thereby avoid redundant feature propagation.

To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data

1 code implementation15 Sep 2023 Hechuan Wen, Tong Chen, Li Kheng Chai, Shazia Sadiq, Kai Zheng, Hongzhi Yin

Due to the imbalanced nature of networked observational data, the causal effect predictions for some individuals can severely violate the positivity/overlap assumption, rendering unreliable estimations.

Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation

1 code implementation7 Sep 2023 Xurong Liang, Tong Chen, Quoc Viet Hung Nguyen, JianXin Li, Hongzhi Yin

In addition, we innovatively design a regularized pruning mechanism in CERP, such that the two sparsified meta-embedding tables are encouraged to encode information that is mutually complementary.

Recommendation Systems

Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation

no code implementations25 Aug 2023 Guanhua Ye, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized IoT service providers have to put unlearning functionality into their consideration.

Machine Unlearning

Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation

1 code implementation24 Aug 2023 Xin Xia, Junliang Yu, Guandong Xu, Hongzhi Yin

On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy.

Session-Based Recommendations

Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph

no code implementations15 Aug 2023 Yi Liu, Hongrui Xuan, Bohan Li, Meng Wang, Tong Chen, Hongzhi Yin

However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement.

Collaborative Filtering Knowledge-Aware Recommendation +2

Graph Condensation for Inductive Node Representation Learning

no code implementations29 Jul 2023 Xinyi Gao, Tong Chen, Yilong Zang, Wentao Zhang, Quoc Viet Hung Nguyen, Kai Zheng, Hongzhi Yin

To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning.

Representation Learning

BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection

no code implementations28 Jul 2023 Jie Liu, Mengting He, Xuequn Shang, Jieming Shi, Bin Cui, Hongzhi Yin

By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies.

CoLA Contrastive Learning +2

HeteFedRec: Federated Recommender Systems with Model Heterogeneity

no code implementations24 Jul 2023 Wei Yuan, Liang Qu, Lizhen Cui, Yongxin Tong, Xiaofang Zhou, Hongzhi Yin

Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems.

Knowledge Distillation Recommendation Systems

Variational Counterfactual Prediction under Runtime Domain Corruption

no code implementations23 Jun 2023 Hechuan Wen, Tong Chen, Li Kheng Chai, Shazia Sadiq, Junbin Gao, Hongzhi Yin

We term the co-occurrence of domain shift and inaccessible variables runtime domain corruption, which seriously impairs the generalizability of a trained counterfactual predictor.

counterfactual Domain Adaptation +1

Personalized Elastic Embedding Learning for On-Device Recommendation

no code implementations18 Jun 2023 Ruiqi Zheng, Liang Qu, Tong Chen, Kai Zheng, Yuhui Shi, Hongzhi Yin

Given a memory budget, PEEL efficiently generates PEEs by selecting embedding blocks with the largest weights, making it adaptable to dynamic memory budgets on devices.

Do as I can, not as I get

no code implementations17 Jun 2023 Shangfei Zheng, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Wei Chen, Lei Zhao

This paper proposes a model called TMR to mine valuable information from simulated data environments.

Knowledge Graphs Multi-modal Knowledge Graph +1

Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs

1 code implementation18 May 2023 Jintang Li, Sheng Tian, Ruofan Wu, Liang Zhu, Welong Zhao, Changhua Meng, Liang Chen, Zibin Zheng, Hongzhi Yin

We approach the problem by our proposed STEP, a self-supervised temporal pruning framework that learns to remove potentially redundant edges from input dynamic graphs.

Dynamic Node Classification

Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures

no code implementations14 May 2023 Wei Yuan, Shilong Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin

Therefore, when incorporating visual information in FedRecs, all existing model poisoning attacks' effectiveness becomes questionable.

Collaborative Filtering Model Poisoning +2

KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment

1 code implementation11 May 2023 Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, Hongzhi Yin

Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set.

Machine Unlearning Response Generation

Explicit Knowledge Graph Reasoning for Conversational Recommendation

no code implementations1 May 2023 Xuhui Ren, Tong Chen, Quoc Viet Hung Nguyen, Lizhen Cui, Zi Huang, Hongzhi Yin

Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions.

Attribute Recommendation Systems

Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling

no code implementations24 Apr 2023 Xuhui Ren, Wei Yuan, Tong Chen, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin

Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences.

Knowledge Graphs Language Modelling +2

Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation

no code implementations9 Apr 2023 Lei Guo, Chunxiao Wang, Xinhua Wang, Lei Zhu, Hongzhi Yin

Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information.

Sequential Recommendation

Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation

no code implementations8 Apr 2023 Jing Long, Tong Chen, Nguyen Quoc Viet Hung, Guandong Xu, Kai Zheng, Hongzhi Yin

In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e. g., dimension \& number of hidden layers).

Knowledge Distillation Privacy Preserving

DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning

no code implementations8 Apr 2023 Shangfei Zheng, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Wei Chen, Lei Zhao

Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability.

Knowledge Graphs Missing Elements +3

Continuous Input Embedding Size Search For Recommender Systems

no code implementations7 Apr 2023 Yunke Qu, Tong Chen, Xiangyu Zhao, Lizhen Cui, Kai Zheng, Hongzhi Yin

Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance.

Recommendation Systems Reinforcement Learning (RL)

Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures

no code implementations6 Apr 2023 Wei Yuan, Quoc Viet Hung Nguyen, Tieke He, Liang Chen, Hongzhi Yin

To reveal the real vulnerability of FedRecs, in this paper, we present a new poisoning attack method to manipulate target items' ranks and exposure rates effectively in the top-$K$ recommendation without relying on any prior knowledge.

Privacy Preserving Recommendation Systems

TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT

no code implementations7 Mar 2023 Yuting Sun, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin

With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices.

Anomaly Detection Time Series +1

Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks

no code implementations27 Feb 2023 Xinyi Gao, Wentao Zhang, Tong Chen, Junliang Yu, Hung Quoc Viet Nguyen, Hongzhi Yin

To tackle the imbalance of minority classes and supplement their inadequate semantics, we present the first method for the semantic imbalance problem in imbalanced HINs named Semantic-aware Node Synthesis (SNS).

Representation Learning

Semi-decentralized Federated Ego Graph Learning for Recommendation

no code implementations10 Feb 2023 Liang Qu, Ningzhi Tang, Ruiqi Zheng, Quoc Viet Hung Nguyen, Zi Huang, Yuhui Shi, Hongzhi Yin

In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner.

Collaborative Filtering Graph Learning +2

Interaction-level Membership Inference Attack Against Federated Recommender Systems

no code implementations26 Jan 2023 Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Lizhen Cui, Tieke He, Hongzhi Yin

An interaction-level membership inference attacker is first designed, and then the classical privacy protection mechanism, Local Differential Privacy (LDP), is adopted to defend against the membership inference attack.

Attribute Federated Learning +3

Knowledge Enhancement for Contrastive Multi-Behavior Recommendation

no code implementations13 Jan 2023 Hongrui Xuan, Yi Liu, Bohan Li, Hongzhi Yin

In particular, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement, and utilize knowledge graph in the knowledge enhancement module to derive more robust knowledge-aware representations for items.

Contrastive Learning Recommendation Systems +1

HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge Tracing

no code implementations23 Dec 2022 Fucai Ke, Weiqing Wang, Weicong Tan, Lan Du, Yuan Jin, Yujin Huang, Hongzhi Yin

Knowledge tracing (KT) aims to leverage students' learning histories to estimate their mastery levels on a set of pre-defined skills, based on which the corresponding future performance can be accurately predicted.

Knowledge Tracing

Efficient Graph Neural Network Inference at Large Scale

no code implementations1 Nov 2022 Xinyi Gao, Wentao Zhang, Yingxia Shao, Quoc Viet Hung Nguyen, Bin Cui, Hongzhi Yin

Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications.

Self-supervised Graph-based Point-of-interest Recommendation

no code implementations22 Oct 2022 Yang Li, Tong Chen, Peng-Fei Zhang, Zi Huang, Hongzhi Yin

In order to counteract the scarcity and incompleteness of POI check-ins, we propose a novel self-supervised learning paradigm in \ssgrec, where the trajectory representations are contrastively learned from two augmented views on geolocations and temporal transitions.

Self-Supervised Learning

Federated Unlearning for On-Device Recommendation

no code implementations20 Oct 2022 Wei Yuan, Hongzhi Yin, Fangzhao Wu, Shijie Zhang, Tieke He, Hao Wang

It removes a user's contribution by rolling back and calibrating the historical parameter updates and then uses these updates to speed up federated recommender reconstruction.

Recommendation Systems

Efficient Bi-Level Optimization for Recommendation Denoising

2 code implementations19 Oct 2022 Zongwei Wang, Min Gao, Wentao Li, Junliang Yu, Linxin Guo, Hongzhi Yin

To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time.

Data Augmentation Denoising +1

Efficient On-Device Session-Based Recommendation

1 code implementation27 Sep 2022 Xin Xia, Junliang Yu, Qinyong Wang, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin

Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item.

Knowledge Distillation Model Compression +1

Switchable Online Knowledge Distillation

1 code implementation12 Sep 2022 Biao Qian, Yang Wang, Hongzhi Yin, Richang Hong, Meng Wang

Instead of focusing on the accuracy gap at test phase by the existing arts, the core idea of SwitOKD is to adaptively calibrate the gap at training phase, namely distillation gap, via a switching strategy between two modes -- expert mode (pause the teacher while keep the student learning) and learning mode (restart the teacher).

Knowledge Distillation

Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation

1 code implementation8 Sep 2022 Ruihong Qiu, Zi Huang, Hongzhi Yin

In this paper, we propose the Overparameterised Recommender (OverRec), which utilises a recurrent neural tangent kernel (RNTK) as a similarity measurement for user sequences to successfully bypass the restriction of hardware for huge models.

Sequential Recommendation

A Survey of Machine Unlearning

1 code implementation6 Sep 2022 Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung Nguyen

Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications.

Attribute Machine Unlearning

XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation

1 code implementation6 Sep 2022 Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, Hongzhi Yin

Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance.

Contrastive Learning

Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction

1 code implementation3 Sep 2022 Yufeng Zhang, Weiqing Wang, Hongzhi Yin, Pengpeng Zhao, Wei Chen, Lei Zhao

A more challenging scenario is that emerging KGs consist of only unseen entities, called as disconnected emerging KGs (DEKGs).

Contrastive Learning Inductive Link Prediction +2

MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning

no code implementations3 Sep 2022 Shangfei Zheng, Weiqing Wang, Jianfeng Qu, Hongzhi Yin, Wei Chen, Lei Zhao

Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i. e., texts and images), which enhance the diversity of knowledge.

Knowledge Graphs Missing Elements +1

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

no code implementations17 Jul 2022 Thanh Tam Nguyen, Thanh Cong Phan, Minh Hieu Nguyen, Matthias Weidlich, Hongzhi Yin, Jun Jo, Quoc Viet Hung Nguyen

Since the spread of rumours in social media is commonly modelled using feature-annotated graphs, we propose a query-by-example approach that, given a rumour graph, extracts the $k$ most similar and diverse subgraphs from past rumours.

Graph Representation Learning Rumour Detection

Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution

no code implementations1 Jul 2022 Yu Yang, Hongzhi Yin, Jiannong Cao, Tong Chen, Quoc Viet Hung Nguyen, Xiaofang Zhou, Lei Chen

Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information.

Dynamic graph embedding Graph Mining

Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation

1 code implementation16 Jun 2022 Lei Guo, Jinyu Zhang, Li Tang, Tong Chen, Lei Zhu, Hongzhi Yin

Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains.

Representation Learning Sequential Recommendation +1

Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation

1 code implementation16 Jun 2022 Lei Guo, Jinyu Zhang, Tong Chen, Xinhua Wang, Hongzhi Yin

Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential recommendation.

Hierarchical Reinforcement Learning reinforcement-learning +2

Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attacks

no code implementations24 May 2022 Shijie Zhang, Wei Yuan, Hongzhi Yin

In this paper, we first design a novel attribute inference attacker to perform a comprehensive privacy analysis of the state-of-the-art federated recommender models.

Attribute Inference Attack +2

Detecting Rumours with Latency Guarantees using Massive Streaming Data

no code implementations13 May 2022 Thanh Tam Nguyen, Thanh Trung Huynh, Hongzhi Yin, Matthias Weidlich, Thanh Thi Nguyen, Thai Son Mai, Quoc Viet Hung Nguyen

Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate.

Rumour Detection

Spatial-Temporal Meta-path Guided Explainable Crime Prediction

no code implementations4 May 2022 Yuting Sun, Tong Chen, Hongzhi Yin

Exposure to crime and violence can harm individuals' quality of life and the economic growth of communities.

BIG-bench Machine Learning Crime Prediction

On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation

1 code implementation23 Apr 2022 Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu, Nguyen Quoc Viet Hung

Meanwhile, to compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher).

Knowledge Distillation Recommendation Systems +1

Single-shot Embedding Dimension Search in Recommender System

no code implementations7 Apr 2022 Liang Qu, Yonghong Ye, Ningzhi Tang, Lixin Zhang, Yuhui Shi, Hongzhi Yin

In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems.

Click-Through Rate Prediction Recommendation Systems

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

1 code implementation6 Apr 2022 Tong Chen, Hongzhi Yin, Jing Long, Quoc Viet Hung Nguyen, Yang Wang, Meng Wang

Such user and group preferences are commonly represented as points in the vector space (i. e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs.

Decision Making

Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

no code implementations1 Apr 2022 Yan Zhang, Changyu Li, Ivor W. Tsang, Hui Xu, Lixin Duan, Hongzhi Yin, Wen Li, Jie Shao

Motivated by the idea of meta-augmentation, in this paper, by treating a user's preference over items as a task, we propose a so-called Diverse Preference Augmentation framework with multiple source domains based on meta-learning (referred to as MetaDPA) to i) generate diverse ratings in a new domain of interest (known as target domain) to handle overfitting on the case of sparse interactions, and to ii) learn a preference model in the target domain via a meta-learning scheme to alleviate cold-start issues.

Domain Adaptation Meta-Learning +1

Decentralized Collaborative Learning Framework for Next POI Recommendation

no code implementations30 Mar 2022 Jing Long, Tong Chen, Nguyen Quoc Viet Hung, Hongzhi Yin

On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner.

Privacy Preserving

Self-Supervised Learning for Recommender Systems: A Survey

1 code implementation29 Mar 2022 Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, Zi Huang

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data.

Recommendation Systems Self-Supervised Learning

AutoML for Deep Recommender Systems: A Survey

no code implementations25 Mar 2022 Ruiqi Zheng, Liang Qu, Bin Cui, Yuhui Shi, Hongzhi Yin

To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems.

AutoML feature selection +1

Towards Revenue Maximization with Popular and Profitable Products

no code implementations26 Feb 2022 Wensheng Gan, Guoting Chen, Hongzhi Yin, Philippe Fournier-Viger, Chien-Ming Chen, Philip S. Yu

To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.

Marketing

Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion

no code implementations19 Feb 2022 Shiqi Wang, Chongming Gao, Min Gao, Junliang Yu, Zongwei Wang, Hongzhi Yin

By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy.

Marketing reinforcement-learning +1

Unified Question Generation with Continual Lifelong Learning

no code implementations24 Jan 2022 Wei Yuan, Hongzhi Yin, Tieke He, Tong Chen, Qiufeng Wang, Lizhen Cui

To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats.

Question Answering Question Generation +1

LECF: Recommendation via Learnable Edge Collaborative Filtering

1 code implementation Science China Information Sciences 2021 Shitao Xiao, Yingxia Shao, Yawen Li, Hongzhi Yin, Yanyan Shen & Bin Cui

In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering (LECF).

Collaborative Filtering

Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent

no code implementations17 Dec 2021 Guanhua Ye, Hongzhi Yin, Tong Chen, Miao Xu, Quoc Viet Hung Nguyen, Jiangning Song

Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating.

Benchmarking Fairness +1

Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation

1 code implementation16 Dec 2021 Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, Quoc Viet Hung Nguyen

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue.

Contrastive Learning Recommendation Systems

Self-supervised Graph Learning for Occasional Group Recommendation

no code implementations4 Dec 2021 Bowen Hao, Hongzhi Yin, Cuiping Li, Hong Chen

As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high-quality group representations.

Contrastive Learning Graph Learning +3

A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation

no code implementations4 Dec 2021 Bowen Hao, Hongzhi Yin, Jing Zhang, Cuiping Li, Hong Chen

In terms of the pretext task, in addition to considering the intra-correlations of users and items by the embedding reconstruction task, we add embedding contrastive learning task to capture inter-correlations of users and items.

Contrastive Learning Meta-Learning +1

A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures

1 code implementation25 Nov 2021 Minh Tam Pham, Thanh Trung Huynh, Van Vinh Tong, Thanh Tam Nguyen, Thanh Thi Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen

In recent years, visual forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security.

Benchmarking DeepFake Detection +2

PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion

no code implementations21 Oct 2021 Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Quoc Viet Hung Nguyen, Lizhen Cui

Evaluations on two real-world datasets show that 1) our attack model significantly boosts the exposure rate of the target item in a stealthy way, without harming the accuracy of the poisoned recommender; and 2) existing defenses are not effective enough, highlighting the need for new defenses against our local model poisoning attacks to federated recommender systems.

Federated Learning Model Poisoning +1

Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

2 code implementations12 Oct 2021 Ruihong Qiu, Zi Huang, Hongzhi Yin, Zijian Wang

In this paper, both empirical and theoretical investigations of this representation degeneration problem are first provided, based on which a novel recommender model DuoRec is proposed to improve the item embeddings distribution.

Contrastive Learning Sequential Recommendation

Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

1 code implementation9 Sep 2021 Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, Hongzhi Yin

Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups.

Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation

2 code implementations1 Sep 2021 Ruihong Qiu, Zi Huang, Hongzhi Yin

In this paper, we propose a novel sequential recommendation framework to overcome these challenges based on a memory augmented multi-instance contrastive predictive coding scheme, denoted as MMInfoRec.

Contrastive Learning Sequential Recommendation

Lightweight Self-Attentive Sequential Recommendation

no code implementations25 Aug 2021 Yang Li, Tong Chen, Peng-Fei Zhang, Hongzhi Yin

Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks.

Sequential Recommendation

Self-Supervised Graph Co-Training for Session-based Recommendation

2 code implementations24 Aug 2021 Xin Xia, Hongzhi Yin, Junliang Yu, Yingxia Shao, Lizhen Cui

In this paper, for informative session-based data augmentation, we combine self-supervised learning with co-training, and then develop a framework to enhance session-based recommendation.

Contrastive Learning Data Augmentation +2

CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation

1 code implementation6 Jul 2021 Ruihong Qiu, Sen Wang, Zhi Chen, Hongzhi Yin, Zi Huang

Existing visually-aware models make use of the visual features as a separate collaborative signal similarly to other features to directly predict the user's preference without considering a potential bias, which gives rise to a visually biased recommendation.

counterfactual Counterfactual Inference +1

Exploiting Positional Information for Session-based Recommendation

no code implementations2 Jul 2021 Ruihong Qiu, Zi Huang, Tong Chen, Hongzhi Yin

According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session.

Session-Based Recommendations

Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks

no code implementations2 Jul 2021 Ruihong Qiu, Zi Huang, Jingjing Li, Hongzhi Yin

Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i. e., a sequence of interactions between a user and multiple items within a period, to preserve the user's recent interest.

Representation Learning Session-Based Recommendations

Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

no code implementations30 Jun 2021 Yang Li, Tong Chen, Yadan Luo, Hongzhi Yin, Zi Huang

Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation.

Multi-Task Learning

Socially-Aware Self-Supervised Tri-Training for Recommendation

1 code implementation7 Jun 2021 Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, Nguyen Quoc Viet Hung

Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected.

Contrastive Learning Recommendation Systems +2

ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks

1 code implementation5 Jun 2021 Liang Qu, Huaisheng Zhu, Ruiqi Zheng, Yuhui Shi, Hongzhi Yin

Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection.

Attribute Classification +3

Learning Elastic Embeddings for Customizing On-Device Recommenders

no code implementations4 Jun 2021 Tong Chen, Hongzhi Yin, Yujia Zheng, Zi Huang, Yang Wang, Meng Wang

The core idea is to compose elastic embeddings for each item, where an elastic embedding is the concatenation of a set of embedding blocks that are carefully chosen by an automated search function.

Recommendation Systems

Learning to Ask Appropriate Questions in Conversational Recommendation

no code implementations11 May 2021 Xuhui Ren, Hongzhi Yin, Tong Chen, Hao Wang, Zi Huang, Kai Zheng

Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations.

Question Generation Question-Generation +1

DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

no code implementations7 May 2021 Lei Guo, Li Tang, Tong Chen, Lei Zhu, Quoc Viet Hung Nguyen, Hongzhi Yin

Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains.

Sequential Recommendation Transfer Learning

Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling

no code implementations5 Apr 2021 Tong Chen, Hongzhi Yin, Xiangliang Zhang, Zi Huang, Yang Wang, Meng Wang

As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.

Feature Engineering

Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning

no code implementations4 Apr 2021 Tong Chen, Hongzhi Yin, Jie Ren, Zi Huang, Xiangliang Zhang, Hao Wang

In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes.

Graph Representation Learning Transductive Learning

Fast-adapting and Privacy-preserving Federated Recommender System

no code implementations2 Apr 2021 Qinyong Wang, Hongzhi Yin, Tong Chen, Junliang Yu, Alexander Zhou, Xiangliang Zhang

In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem.

Federated Learning Meta-Learning +2

Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation

no code implementations24 Mar 2021 Lei Guo, Hongzhi Yin, Tong Chen, Xiangliang Zhang, Kai Zheng

However, the representation learning for a group is most complex beyond the fusion of group member representation, as the personal preferences and group preferences may be in different spaces.

Representation Learning

Graph Embedding for Recommendation against Attribute Inference Attacks

no code implementations29 Jan 2021 Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui, Xiangliang Zhang

Specifically, in GERAI, we bind the information perturbation mechanism in differential privacy with the recommendation capability of graph convolutional networks.

Attribute Graph Embedding +2

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

4 code implementations16 Jan 2021 Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, Xiangliang Zhang

In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations.

Recommendation Systems Self-Supervised Learning

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

no code implementations8 Jan 2021 Guanhua Ye, Hongzhi Yin, Tong Chen, Hongxu Chen, Lizhen Cui, Xiangliang Zhang

Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings.

Sleep apnea detection

Temporal Meta-path Guided Explainable Recommendation

1 code implementation5 Jan 2021 Hongxu Chen, Yicong Li, Xiangguo Sun, Guandong Xu, Hongzhi Yin

This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations.

Social and Information Networks

Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph

no code implementations4 Jan 2021 Yuandong Wang, Hongzhi Yin, Tong Chen, Chunyang Liu, Ben Wang, Tianyu Wo, Jie Xu

Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i. e., weights) of passenger demands between two connected regions.

Graph Attention Representation Learning +1

Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach

1 code implementation28 Dec 2020 Bowen Hao, Jing Zhang, Cuiping Li, Hong Chen, Hongzhi Yin

On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models.

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

2 code implementations12 Dec 2020 Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, Xiangliang Zhang

Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task.

Self-Supervised Learning Session-Based Recommendations

Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks

1 code implementation17 Nov 2020 Tam Thanh Nguyen, Thanh Trung Huynh, Hongzhi Yin, Vinh Van Tong, Darnbi Sakong, Bolong Zheng, Quoc Viet Hung Nguyen

Knowledge graphs (KGs) have become popular structures for unifying real-world entities by modelling the relationships between them and their attributes.

Ranked #7 on Entity Alignment on DBP15k zh-en (using extra training data)

Attribute Entity Alignment +1

Deep Pairwise Hashing for Cold-start Recommendation

no code implementations2 Nov 2020 Yan Zhang, Ivor W. Tsang, Hongzhi Yin, Guowu Yang, Defu Lian, Jingjing Li

Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation.

Denoising

Overcoming Data Sparsity in Group Recommendation

no code implementations2 Oct 2020 Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Xiaofang Zhou

Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the limitation and sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members.

Decision Making Graph Embedding +2

GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation

1 code implementation6 Jul 2020 Ruihong Qiu, Hongzhi Yin, Zi Huang, Tong Chen

On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user.

Attribute Session-Based Recommendations

EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors

no code implementations6 Jun 2020 Yu Yang, Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Hongzhi Yin, Xiaofang Zhou

We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline learning behaviors.

Management Network Embedding

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

no code implementations2 Jun 2020 Hongxu Chen, Hongzhi Yin, Xiangguo Sun, Tong Chen, Bogdan Gabrys, Katarzyna Musial

Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks.

Anchor link prediction Model Selection

GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection

1 code implementation20 May 2020 Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, Lizhen Cui

Therefore, it is of great practical significance to construct a robust recommender system that is able to generate stable recommendations even in the presence of shilling attacks.

Recommendation Systems Representation Learning

Try This Instead: Personalized and Interpretable Substitute Recommendation

no code implementations19 May 2020 Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang, Meng Wang

Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i. e., personalization) and item-item relationships (i. e., substitution) for recommendation.

Attribute Collaborative Filtering +1

Enhancing Social Recommendation with Adversarial Graph Convolutional Networks

no code implementations5 Apr 2020 Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, Lizhen Cui

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data.

Recommendation Systems

A Block-based Generative Model for Attributed Networks Embedding

no code implementations6 Jan 2020 Xueyan Liu, Bo Yang, Wenzhuo Song, Katarzyna Musial, Wanli Zuo, Hongxu Chen, Hongzhi Yin

To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block.

Attribute Clustering +1

Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks

1 code implementation27 Nov 2019 Ruihong Qiu, Jingjing Li, Zi Huang, Hongzhi Yin

In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system.

Graph Classification Session-Based Recommendations

Sequence-Aware Factorization Machines for Temporal Predictive Analytics

no code implementations7 Nov 2019 Tong Chen, Hongzhi Yin, Quoc Viet Hung Nguyen, Wen-Chih Peng, Xue Li, Xiaofang Zhou

As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics.

Recommendation Systems Test

Unsupervised Learning of Node Embeddings by Detecting Communities

no code implementations25 Sep 2019 Chi Thang Duong, Dung Hoang, Truong Giang Le Ba, Thanh Le Cong, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer

We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.

Clustering Node Classification +1

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

no code implementations8 Sep 2019 Junliang Yu, Min Gao, Hongzhi Yin, Jundong Li, Chongming Gao, Qinyong Wang

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems.

Recommendation Systems

Parallel Computation of Graph Embeddings

no code implementations6 Sep 2019 Chi Thang Duong, Hongzhi Yin, Thanh Dat Hoang, Truong Giang Le Ba, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer

We therefore propose a framework for parallel computation of a graph embedding using a cluster of compute nodes with resource constraints.

Graph Embedding

TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs

no code implementations6 Jul 2019 Saeid Hosseini, Saeed Najafipour, Ngai-Man Cheung, Hongzhi Yin, Mohammad Reza Kangavari, Xiaofang Zhou

We can use the temporal and textual data of the nodes to compute the edge weights and then generate subgraphs with highly relevant nodes.

Few-Shot Deep Adversarial Learning for Video-based Person Re-identification

no code implementations29 Mar 2019 Lin Wu, Yang Wang, Hongzhi Yin, Meng Wang, Ling Shao

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages.

Time Series Time Series Analysis +1

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

no code implementations13 Dec 2018 Mingyue Shang, Zhenxin Fu, Hongzhi Yin, Bo Tang, Dongyan Zhao, Rui Yan

In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).

Cloze Test Natural Language Inference +3

Look Deeper See Richer: Depth-aware Image Paragraph Captioning

no code implementations ACM International Conference on Multimedia 2018 Ziwei Wang, Yadan Luo, Yang Li, Zi Huang, Hongzhi Yin

Existing image paragraph captioning methods give a series of sentences to represent the objects and regions of interests, where the descriptions are essentially generated by feeding the image fragments containing objects and regions into conventional image single-sentence captioning models.

Image Captioning Image Paragraph Captioning +1

Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection

no code implementations20 Apr 2017 Tong Chen, Lin Wu, Xue Li, Jun Zhang, Hongzhi Yin, Yang Wang

The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time.

Deep Attention

Personalized Video Recommendation Using Rich Contents from Videos

1 code implementation21 Dec 2016 Xingzhong Du, Hongzhi Yin, Ling Chen, Yang Wang, Yi Yang, Xiaofang Zhou

In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features.

Recommendation Systems

Cannot find the paper you are looking for? You can Submit a new open access paper.