OpenRaiser/Intern-Atlas huggingface dataset:
Hierarchical personalized federated learning for user modeling
Authors: Jinze Wu, Qi Liu, Zhenya Huang, Yuting Ning, Hao Wang, Enhong Chen, Jinfeng Yi, Bowen Zhou
Venue: Conference WWW 2021
Year: 2021

User modeling aims to capture the latent characteristics of users from their behaviors, and is widely applied in numerous applications. Usually, centralized user modeling suffers from the risk of privacy leakage. Instead, federated user modeling expects to provide a secure multi-client collaboration for user modeling through federated learning. Existing federated learning methods are mainly designed for consistent clients, which cannot be directly applied to practical scenarios, where different clients usually store inconsistent user data. Therefore, it is a crucial demand to design an appropriate federated solution that can better adapt to user modeling tasks, and however, meets following critical challenges: 1) Statistical heterogeneity. The distributions of user data in different clients are not always independently identically distributed which leads to personalized clients; 2) Privacy heterogeneity. User data contains both public and private information, which have different levels of privacy. It means we should balance different information to be shared and protected; 3) Model heterogeneity. The local user models trained with client records are heterogeneous which need flexible aggregation in the server. In this paper, we propose a novel client-server architecture framework, namely Hierarchical Personalized Federated Learning (HPFL) to serve federated learning in user modeling with inconsistent clients. In the framework, we first define hierarchical information to finely partition the data with privacy heterogeneity. On this basis, the client trains a user model which contains different components designed for hierarchical information. Moreover, client processes a fine-grained personalized update strategy to update personalized user model for statistical heterogeneity. Correspondingly, the server completes a differentiated component aggregation strategy to flexibly aggregate heterogeneous user models in the case of privacy and model heterogeneity. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of the HPFL framework.
Sampler design for implicit feedback data by noisy-label robust learning
Authors: Wenhui Yu, Zheng Qin 0003
Venue: Conference SIGIR 2020
Year: 2020

Implicit feedback data is extensively explored in recommendation as it is easy to collect and generally applicable. However, predicting users' preference on implicit feedback data is a challenging task since we can only observe positive (voted) samples and unvoted samples. It is difficult to distinguish between the negative samples and unlabeled positive samples from the unvoted ones. Existing works, such as Bayesian Personalized Ranking (BPR), sample unvoted items as negative samples uniformly, therefore suffer from a critical noisy-label issue. To address this gap, we design an adaptive sampler based on noisy-label robust learning for implicit feedback data. To formulate the issue, we first introduce Bayesian Point-wise Optimization (BPO) to learn a model, e.g., Matrix Factorization (MF), by maximum likelihood estimation. We predict users' preferences with the model and learn it by maximizing likelihood of observed data labels, i.e., a user prefers her positive samples and has no interests in her unvoted samples. However, in reality, a user may have interests in some of her unvoted samples, which are indeed positive samples mislabeled as negative ones. We then consider the risk of these noisy labels, and propose a Noisy-label Robust BPO (NBPO). NBPO also maximizes the observation likelihood while connects users' preference and observed labels by the likelihood of label flipping based on the Bayes' theorem. In NBPO, a user prefers her true positive samples and shows no interests in her true negative samples, hence the optimization quality is dramatically improved. Extensive experiments on two public real-world datasets show the significant improvement of our proposed optimization methods.
Pipattack: Poisoning federated recommender systems for manipulating item promotion
Authors: Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Quoc Viet Hung Nguyen, Lizhen Cui
Venue: Conference WSDM 2022
Year: 2022

Due to the growing privacy concerns, decentralization emerges rapidly in personalized services, especially recommendation. Also, recent studies have shown that centralized models are vulnerable to poisoning attacks, compromising their integrity. In the context of recommender systems, a typical goal of such poisoning attacks is to promote the adversary's target items by interfering with the training dataset and/or process. Hence, a common practice is to subsume recommender systems under the decentralized federated learning paradigm, which enables all user devices to collaboratively learn a global recommender while retaining all the sensitive data locally. Without exposing the full knowledge of the recommender and entire dataset to end-users, such federated recommendation is widely regarded 'safe' towards poisoning attacks. In this paper, we present a systematic approach to backdooring federated recommender systems for targeted item promotion. The core tactic is to take advantage of the inherent popularity bias that commonly exists in data-driven recommenders. As popular items are more likely to appear in the recommendation list, our innovatively designed attack model enables the target item to have the characteristics of popular items in the embedding space. Then, by uploading carefully crafted gradients via a small number of malicious users during the model update, we can effectively increase the exposure rate of a target (unpopular) item in the resulted federated recommender. 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.
All you need is low (rank) defending against adversarial attacks on graphs
Authors: Negin Entezari, Saba A. Al-Sayouri, Amirali Darvishzadeh, Evangelos E. Papalexakis
Venue: Conference WSDM 2020
Year: 2020

Recent studies have demonstrated that machine learning approaches like deep learning methods are easily fooled by adversarial attacks. Recently, a highly-influential study examined the impact of adversarial attacks on graph data and demonstrated that graph embedding techniques are also vulnerable to adversarial attacks. Fake users on social media and fake product reviews are examples of perturbations in graph data that are realistic counterparts of the adversarial models proposed. Graphs are widely used in a variety of domains and it is highly important to develop graph analysis techniques that are robust to adversarial attacks. One of the recent studies on generating adversarial attacks for graph data is Nettack. The Nettack model has shown to be very successful in deceiving the Graph Convolutional Network (GCN) model. Nettack is also transferable to other node classification approaches e.g. node embeddings. In this paper, we explore the properties of Nettack perturbations, in search for effective defenses against them. Our first finding is that Nettack demonstrates a very specific behavior in the spectrum of the graph: only high-rank (low-valued) singular components of the graph are affected. Following that insight, we show that a low-rank approximation of the graph, that uses only the top singular components for its reconstruction, can greatly reduce the effects of Nettack and boost the performance of GCN when facing adversarial attacks. Indicatively, on the CiteSeer dataset, our proposed defense mechanism is able to reduce the success rate of Nettack from 98% to 36%. Furthermore, we show that tensor-based node embeddings, which by default project the graph into a low-rank subspace, are robust against Nettack perturbations. Lastly, we propose LowBlow, a low-rank adversarial attack which is able to affect the classification performance of both GCN and tensor-based node embeddings and we show that the low-rank attack is noticeable and making it unnoticeable results in a high-rank attack.
Node similarity preserving graph convolutional networks
Authors: Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, Jiliang Tang
Venue: Conference WSDM 2021
Year: 2021

Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the graph structure and node features by aggregating and transforming information within node neighborhoods. However, through theoretical and empirical analysis, we reveal that the aggregation process of GNNs tends to destroy node similarity in the original feature space. There are many scenarios where node similarity plays a crucial role. Thus, it has motivated the proposed framework SimP-GCN that can effectively and efficiently preserve node similarity while exploiting graph structure. Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features. Furthermore, we employ self-supervised learning to explicitly capture the complex feature similarity and dissimilarity relations between nodes. We validate the effectiveness of SimP-GCN on seven benchmark datasets including three assortative and four disassorative graphs. The results demonstrate that SimP-GCN outperforms representative baselines. Further probe shows various advantages of the proposed framework. The implementation of SimP-GCN is available at \url{https://github.com/ChandlerBang/SimP-GCN}.
Online Multi-modal Hashing with Dynamic Query-adaption
Authors: Xu Lu, Lei Zhu, Zhiyong Cheng, Liqiang Nie, Huaxiang Zhang
Venue: Conference SIGIR 2019
Year: 2019

Multi-modal hashing is an effective technique to support large-scale multimedia retrieval, due to its capability of encoding heterogeneous multi-modal features into compact and similarity-preserving binary codes. Although great progress has been achieved so far, existing methods still suffer from several problems, including: 1) All existing methods simply adopt fixed modality combination weights in online hashing process to generate the query hash codes. This strategy cannot adaptively capture the variations of different queries. 2) They either suffer from insufficient semantics (for unsupervised methods) or require high computation and storage cost (for the supervised methods, which rely on pair-wise semantic matrix). 3) They solve the hash codes with relaxed optimization strategy or bit-by-bit discrete optimization, which results in significant quantization loss or consumes considerable computation time. To address the above limitations, in this paper, we propose an Online Multi-modal Hashing with Dynamic Query-adaption (OMH-DQ) method in a novel fashion. Specifically, a self-weighted fusion strategy is designed to adaptively preserve the multi-modal feature information into hash codes by exploiting their complementarity. The hash codes are learned with the supervision of pair-wise semantic labels to enhance their discriminative capability, while avoiding the challenging symmetric similarity matrix factorization. Under such learning framework, the binary hash codes can be directly obtained with efficient operations and without quantization errors. Accordingly, our method can benefit from the semantic labels, and simultaneously, avoid the high computation complexity. Moreover, to accurately capture the query variations, at the online retrieval stage, we design a parameter-free online hashing module which can adaptively learn the query hash codes according to the dynamic query contents. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach from various aspects.
Full dof tracking of a hand interacting with an object by modeling occlusions and physical constraints
Authors: Iasonas Oikonomidis, Nikolaos Kyriazis, Antonis A. Argyros
Venue: Conference ICCV 2011
Year: 2011

Due to occlusions, the estimation of the full pose of a human hand interacting with an object is much more challenging than pose recovery of a hand observed in isolation. In this work we formulate an optimization problem whose solution is the 26-DOF hand pose together with the pose and model parameters of the manipulated object. Optimization seeks for the joint hand-object model that (a) best explains the incompleteness of observations resulting from occlusions due to hand-object interaction and (b) is physically plausible in the sense that the hand does not share the same physical space with the object. The proposed method is the first that solves efficiently the continuous, full-DOF, joint hand-object tracking problem based solely on markerless multicamera input. Additionally, it is the first to demonstrate how hand-object interaction can be exploited as a context that facilitates hand pose estimation, instead of being considered as a complicating factor. Extensive quantitative and qualitative experiments with simulated and real world image sequences as well as a comparative evaluation with a state-of-the-art method for pose estimation of isolated hands, support the above findings.
Conet: Collaborative cross networks for cross-domain recommendation
Authors: Guangneng Hu, Yu Zhang, Qiang Yang
Venue: Conference CIKM 2018
Year: 2018

The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains. Transfer learning is a class of algorithms underlying these techniques. In this paper, we propose a novel transfer learning approach for cross-domain recommendation by using neural networks as the base model. In contrast to the matrix factorization based cross-domain techniques, our method is deep transfer learning, which can learn complex user-item interaction relationships. We assume that hidden layers in two base networks are connected by cross mappings, leading to the collaborative cross networks (CoNet). CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa. CoNet is achieved in multi-layer feedforward networks by adding dual connections and joint loss functions, which can be trained efficiently by back-propagation. The proposed model is thoroughly evaluated on two large real-world datasets. It outperforms baselines by relative improvements of 7.84\% in NDCG. We demonstrate the necessity of adaptively selecting representations to transfer. Our model can reduce tens of thousands training examples comparing with non-transfer methods and still has the competitive performance with them.
DDTCDR: Deep dual transfer cross domain recommendation
Authors: Pan Li, Alexander Tuzhilin
Venue: Conference WSDM 2020
Year: 2020

Cross domain recommender systems have been increasingly valuable for helping consumers identify the most satisfying items from different categories. However, previously proposed cross-domain models did not take into account bidirectional latent relations between users and items. In addition, they do not explicitly model information of user and item features, while utilizing only user ratings information for recommendations. To address these concerns, in this paper we propose a novel approach to cross-domain recommendations based on the mechanism of dual learning that transfers information between two related domains in an iterative manner until the learning process stabilizes. We develop a novel latent orthogonal mapping to extract user preferences over multiple domains while preserving relations between users across different latent spaces. Combining with autoencoder approach to extract the latent essence of feature information, we propose Deep Dual Transfer Cross Domain Recommendation (DDTCDR) model to provide recommendations in respective domains. We test the proposed method on a large dataset containing three domains of movies, book and music items and demonstrate that it consistently and significantly outperforms several state-of-the-art baselines and also classical transfer learning approaches.
Arnetminer: extraction and mining of academic social networks
Authors: Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, Zhong Su
Venue: Conference KDD 2008
Year: 2008

This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.
Investigating and Mitigating Degree- Related Biases in Graph Convoltuional Networks
Authors: Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu C. Aggarwal, Prasenjit Mitra, Suhang Wang
Venue: Conference CIKM 2020
Year: 2020

Graph Convolutional Networks (GCNs) show promising results for semi-supervised learning tasks on graphs, thus become favorable comparing with other approaches. Despite the remarkable success of GCNs, it is difficult to train GCNs with insufficient supervision. When labeled data are limited, the performance of GCNs becomes unsatisfying for low-degree nodes. While some prior work analyze successes and failures of GCNs on the entire model level, profiling GCNs on individual node level is still underexplored. In this paper, we analyze GCNs in regard to the node degree distribution. From empirical observation to theoretical proof, we confirm that GCNs are biased towards nodes with larger degrees with higher accuracy on them, even if high-degree nodes are underrepresented in most graphs. We further develop a novel Self-Supervised-Learning Degree-Specific GCN (SL-DSGCN) that mitigate the degree-related biases of GCNs from model and data aspects. Firstly, we propose a degree-specific GCN layer that captures both discrepancies and similarities of nodes with different degrees, which reduces the inner model-aspect biases of GCNs caused by sharing the same parameters with all nodes. Secondly, we design a self-supervised-learning algorithm that creates pseudo labels with uncertainty scores on unlabeled nodes with a Bayesian neural network. Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective. Uncertainty scores are further exploited to weight pseudo labels dynamically in the stochastic gradient descent for SL-DSGCN. Experiments on three benchmark datasets show SL-DSGCN not only outperforms state-of-the-art self-training/self-supervised-learning GCN methods, but also improves GCN accuracy dramatically for low-degree nodes.
Indirect invisible poisoning attacks on domain adaptation
Authors: Jun Wu, Jingrui He
Venue: Conference KDD 2021
Year: 2021

Unsupervised domain adaptation has been successfully applied across multiple high-impact applications, since it improves the generalization performance of a learning algorithm when the source and target domains are related. However, the adversarial vulnerability of domain adaptation models has largely been neglected. Most existing unsupervised domain adaptation algorithms might be easily fooled by an adversary, resulting in deteriorated prediction performance on the target domain, when transferring the knowledge from a maliciously manipulated source domain. To demonstrate the adversarial vulnerability of existing domain adaptation techniques, in this paper, we propose a generic data poisoning attack framework named I2Attack for domain adaptation with the following properties: (1) perceptibly unnoticeable: all the poisoned inputs are natural-looking; (2)adversarially indirect: only source examples are maliciously manipulated; (3) algorithmically invisible: both source classification error and marginal domain discrepancy between source and target domains will not increase. Specifically, it aims to degrade the overall prediction performance on the target domain by maximizing the label-informed domain discrepancy over both input feature space and class-label space be-tween source and target domains. Within this framework, a family of practical poisoning attacks are presented to fool the existing domain adaptation algorithms associated with different discrepancy measures. Extensive experiments on various domain adaptation benchmarks confirm the effectiveness and computational efficiency of our proposed I2Attack framework.
FedCT: Federated Collaborative Transfer for Recommendation
Authors: Shuchang Liu 0001, Shuyuan Xu, Wenhui Yu, Zuohui Fu, Yongfeng Zhang 0003, Amélie Marian
Venue: Conference SIGIR 2021
Year: 2021

When a user starts exploring items from a new area of an e-commerce system, cross-domain recommendation techniques come into help by transferring the abundant knowledge from the user's familiar domains to this new domain. However, this solution usually requires direct information sharing between service providers on the cloud which may not always be available and brings privacy concerns. In this paper, we show that one can overcome these concerns through learning on edge devices such as smartphones and laptops. The cross-domain recommendation problem is formalized under a decentralized computing environment with multiple domain servers. And we identify two key challenges for this setting: the unavailability of direct transfer and the heterogeneity of the domain-specific user representations. We then propose to learn and maintain a decentralized user encoding on each user's personal space. The optimization follows a variational inference framework that maximizes the mutual information between the user's encoding and the domain-specific user information from all her interacted domains. Empirical studies on real-world datasets exhibit the effectiveness of our proposed framework on recommendation tasks and its superiority over domain-pairwise transfer models. The resulting system offers reduced communication cost and an efficient inference mechanism that does not depend on the number of involved domains, and it allows flexible plugin of domain-specific transfer models without significant interference on other domains.