1 code implementation • 23 Apr 2024 • Thanh Toan Nguyen, Quoc Viet Hung Nguyen, Thanh Tam Nguyen, Thanh Trung Huynh, Thanh Thi Nguyen, Matthias Weidlich, Hongzhi Yin
This survey aims to fill this gap by primarily focusing on poisoning attacks and their countermeasures.
no code implementations • 18 Apr 2024 • Liang Qu, Yun Lin, Wei Yuan, Xiaojun Wan, Yuhui Shi, Hongzhi Yin
Given the critical role of similarity metrics in recommender systems, existing methods mainly employ handcrafted similarity metrics to capture the complex characteristics of user-item interactions.
no code implementations • 1 Apr 2024 • Ruiqi Zheng, Liang Qu, Tong Chen, Kai Zheng, Yuhui Shi, Hongzhi Yin
Knowledge sharing also opens a backdoor for model poisoning attacks, where adversaries disguise themselves as benign clients and disseminate polluted knowledge to achieve malicious goals like promoting an item's exposure rate.
1 code implementation • 31 Mar 2024 • Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren, Thanh Toan Nguyen, Phi Le Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies.
no code implementations • 29 Mar 2024 • Wei Yuan, Chaoqun Yang, Liang Qu, Guanhua Ye, Quoc Viet Hung Nguyen, Hongzhi Yin
In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec.
1 code implementation • 27 Mar 2024 • Xurong Liang, Tong Chen, Lizhen Cui, Yang Wang, Meng Wang, Hongzhi Yin
Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods.
1 code implementation • 26 Mar 2024 • Yanran Tang, Ruihong Qiu, Hongzhi Yin, Xue Li, Zi Huang
In a case pool, there are three types of case connectivity relationships: the case reference relationship, the case semantic relationship, and the case legal charge relationship.
1 code implementation • 18 Mar 2024 • Yanling Wang, Jing Zhang, Lingxi Zhang, Lixin Liu, Yuxiao Dong, Cuiping Li, Hong Chen, Hongzhi Yin
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community.
no code implementations • 14 Mar 2024 • Jie Liu, Xuequn Shang, Xiaolin Han, Wentao Zhang, Hongzhi Yin
Then STRIPE incorporates separate spatial and temporal memory networks, which capture and store prototypes of normal patterns, thereby preserving the uniqueness of spatial and temporal normality.
no code implementations • 27 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.
no code implementations • 1 Feb 2024 • Sheng Zhang, Maolin Wang, Yao Zhao, Chenyi Zhuang, Jinjie Gu, Ruocheng Guo, Xiangyu Zhao, Zijian Zhang, Hongzhi Yin
Our research addresses the computational and resource inefficiencies that current Sequential Recommender Systems (SRSs) suffer from.
no code implementations • 31 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.
no code implementations • 29 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.
no code implementations • 26 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.
no code implementations • 26 Jan 2024 • Jing Long, Tong Chen, Guanhua Ye, Kai Zheng, Nguyen Quoc Viet Hung, Hongzhi Yin
Empirical results demonstrate that PTIA poses a significant threat to users' historical trajectories.
1 code implementation • 26 Jan 2024 • Lei Guo, Ziang Lu, Junliang Yu, Nguyen Quoc Viet Hung, Hongzhi Yin
For Limitation 2, we model items in a universal feature space by their description texts.
no code implementations • 24 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.
no code implementations • 22 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).
no code implementations • 21 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.
no code implementations • 16 Jan 2024 • Hao liu, Lei Guo, Lei Zhu, Yongqiang Jiang, Min Gao, Hongzhi Yin
To overcome the above challenges, we focus on NMCR, and devise MCRPL as our solution.
no code implementations • 7 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.
1 code implementation • 3 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).
no code implementations • 25 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.
1 code implementation • 22 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.
no code implementations • 18 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.
no code implementations • 30 Nov 2023 • Zongwei Wang, Junliang Yu, Min Gao, Hongzhi Yin, Bin Cui, Shazia Sadiq
Contrastive learning (CL) has recently gained significant popularity in the field of recommendation.
1 code implementation • 25 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.
1 code implementation • 19 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.
no code implementations • 1 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.
1 code implementation • 25 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.
no code implementations • 23 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.
no code implementations • 20 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.
no code implementations • 17 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.
1 code implementation • 15 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.
1 code implementation • 7 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.
no code implementations • 25 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.
1 code implementation • 24 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.
no code implementations • 15 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.
no code implementations • 29 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.
no code implementations • 28 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.
no code implementations • 24 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.
no code implementations • 23 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.
no code implementations • 18 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.
no code implementations • 17 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.
1 code implementation • 18 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.
no code implementations • 14 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.
1 code implementation • 11 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.
no code implementations • 1 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.
no code implementations • 28 Apr 2023 • Jie Liu, Mengting He, Guangtao Wang, Nguyen Quoc Viet Hung, Xuequn Shang, Hongzhi Yin
minority classes to balance the label and topology distribution.
no code implementations • 24 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.
no code implementations • 9 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.
no code implementations • 8 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).
no code implementations • 8 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.
no code implementations • 7 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.
no code implementations • 6 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.
no code implementations • 7 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.
no code implementations • 27 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).
no code implementations • 10 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.
no code implementations • 26 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.
no code implementations • 13 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.
no code implementations • 23 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.
no code implementations • 1 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.
no code implementations • 22 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.
no code implementations • 20 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.
2 code implementations • 19 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.
1 code implementation • 27 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.
1 code implementation • 12 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).
1 code implementation • 8 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.
1 code implementation • 6 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.
1 code implementation • 6 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.
1 code implementation • 3 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).
no code implementations • 3 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.
no code implementations • 17 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.
no code implementations • 1 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.
1 code implementation • 16 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
1 code implementation • 16 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.
no code implementations • 24 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.
no code implementations • 13 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.
no code implementations • 4 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.
1 code implementation • 23 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).
no code implementations • 7 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.
1 code implementation • 6 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.
no code implementations • 1 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.
no code implementations • 30 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.
1 code implementation • 29 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.
no code implementations • 25 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.
no code implementations • 26 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.
no code implementations • 19 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.
no code implementations • 24 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.
no code implementations • 8 Jan 2022 • Mubashir Imran, Hongzhi Yin, Tong Chen, Zi Huang, Kai Zheng
Such heterogeneous network embedding (HNE) methods effectively harness the heterogeneity of small-scale HINs.
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).
no code implementations • 17 Dec 2021 • Vinh Van Tong, Thanh Trung Huynh, Thanh Tam Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen, Quyet Thang Huynh
Our KG embedding framework exploits two feature channels: transitivity-based and proximity-based.
no code implementations • 17 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.
1 code implementation • 16 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.
no code implementations • 4 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.
no code implementations • 4 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.
1 code implementation • 25 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.
no code implementations • 21 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.
2 code implementations • 12 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.
no code implementations • 14 Sep 2021 • Yuandong Wang, Hongzhi Yin, Lian Wu, Tong Chen, Chunyang Liu
In recent years, online ride-hailing platforms have become an indispensable part of urban transportation.
1 code implementation • 9 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.
2 code implementations • 1 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.
no code implementations • 25 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.
2 code implementations • 24 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.
1 code implementation • 6 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.
no code implementations • 2 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.
no code implementations • 2 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.
no code implementations • 30 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.
1 code implementation • 7 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.
1 code implementation • 5 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.
no code implementations • 4 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.
1 code implementation • 19 May 2021 • Sixiao Zhang, Hongxu Chen, Xiao Ming, Lizhen Cui, Hongzhi Yin, Guandong Xu
Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems.
no code implementations • 11 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.
no code implementations • 7 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.
no code implementations • 5 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.
no code implementations • 4 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.
no code implementations • 2 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.
no code implementations • 24 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.
no code implementations • 29 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.
4 code implementations • 16 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.
no code implementations • 8 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.
1 code implementation • 5 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
no code implementations • 4 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.
1 code implementation • 28 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.
1 code implementation • 13 Dec 2020 • Bowen Hao, Jing Zhang, Hongzhi Yin, Cuiping Li, Hong Chen
Cold-start problem is a fundamental challenge for recommendation tasks.
2 code implementations • 12 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.
1 code implementation • 17 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)
no code implementations • 2 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.
no code implementations • 2 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.
1 code implementation • 6 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.
no code implementations • 6 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.
no code implementations • 2 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.
1 code implementation • 20 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.
no code implementations • 19 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.
no code implementations • 5 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.
no code implementations • 6 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.
1 code implementation • 27 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.
no code implementations • 7 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.
no code implementations • 25 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.
no code implementations • 8 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.
no code implementations • 6 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.
no code implementations • 6 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.
no code implementations • 29 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.
no code implementations • 13 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).
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.
no code implementations • 20 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.
1 code implementation • 21 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.