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Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks

Yali Du, Meng Fang, Jinfeng Yi, Chang Xu, Jun Cheng, Dacheng Tao
2019 IEEE transactions on multimedia  
Recent collaborative filtering methods based on the deep neural network are studied and introduce promising results due to their power in learning hidden representations for users and items.  ...  With the knowledge of a collaborative filtering algorithm and its parameters, the performance of this recommendation system can be easily downgraded.  ...  Index Terms-Recommendation Systems, Adversarial Learning, Collaborative Filtering, Malicious Attacks I.  ... 
doi:10.1109/tmm.2018.2887018 fatcat:ulpj5njdindjrbrqvhqsb2v4fy

Dual Adversarial Variational Embedding for Robust Recommendation [article]

Qiaomin Yi, Ning Yang, Philip S. Yu
2021 arXiv   pre-print
In this paper, we propose a novel model called Dual Adversarial Variational Embedding (DAVE) for robust recommendation, which can provide personalized noise reduction for different users and items, and  ...  Robust recommendation aims at capturing true preference of users from noisy data, for which there are two lines of methods have been proposed.  ...  Traditional Recommender Systems In traditional recommender systems, collaborative filtering is the most widely used technique for personalized recommendation, which aims to predict user preference from  ... 
arXiv:2106.15779v1 fatcat:3776g6w36rfjnnsmhpu3dgfuha

Sparsity Regularization For Cold-Start Recommendation [article]

Aksheshkumar Ajaykumar Shah, Hemanth Venkateswara
2022 arXiv   pre-print
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm  ...  In this paper we introduce a novel representation for user-vectors by combining user demographics and user preferences, making the model a hybrid system which uses Collaborative Filtering and Content Based  ...  Generative Adversarial Networks based Collaborative Filtering Methods In domain of recommendation systems Generative Adversarial Networks have also been implemented by some methods for Collaborative filtering  ... 
arXiv:2201.10711v3 fatcat:o24kxcr7pra6hkx7jliakee66u

A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks [article]

Yashar Deldjoo and Tommaso Di Noia and Felice Antonio Merra
2020 arXiv   pre-print
successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions.  ...  Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance  ...  Section 2) and, (ii) AML used in generative adversarial networks (GANs) exploited for numerous tasks such as better CF recommendation, context-aware recommendation, cross-domain system, or visually-aware  ... 
arXiv:2005.10322v2 fatcat:4wqcluqgnbbwpkicunn42et5te

Introduction to the Special Section on Artificial Intelligence Security: Adversarial Attack and Defense

Xiaojiang Du, Willy Susilo, Mohsen Guizani, Zhihong Tian
2021 IEEE Transactions on Network Science and Engineering  
Bosri et al. in "Integrating Blockchain with Artificial Intelligence for Privacy-Preserving in Recommender Systems" solved the problem in the current recommender system, which was collecting customer's  ...  Patel et al. in "KiRTi: A Blockchain-based Credit Recommender System for Financial Institutions" propose KiRTi, a deep-learning-based credit-recommender scheme for public blockchain to facilitate smart  ... 
doi:10.1109/tnse.2021.3073637 fatcat:ib5qh53qq5bu5hrfjejm3fp76i

On Estimating the Training Cost of Conversational Recommendation Systems [article]

Stefanos Antaris, Dimitrios Rafailidis, Mohammad Aliannejadi
2020 arXiv   pre-print
Conversational recommendation systems have recently gain a lot of attention, as users can continuously interact with the system over multiple conversational turns.  ...  However, conversational recommendation systems are based on complex neural architectures, thus the training cost of such models is high.  ...  In particular, the personalized recommendations are generated by the user-based autoencoder for collaborative filtering (U-Autorec), presented in [23] , a model to predict ratings for users not observed  ... 
arXiv:2011.05302v1 fatcat:kr7ua6ipn5fhrgkqdydwe426oe

Special Issue on Robustness and Efficiency in the Convergence of Artificial Intelligence and IoT

Meikang Qiu, Bhavani Thuraisingham, Mahmoud Daneshmand, Huansheng Ning, Payam Barnaghi
2021 IEEE Internet of Things Journal  
collaborative systems.  ...  fusion model for POI recommendation in the context of location-based social networks.  ... 
doi:10.1109/jiot.2021.3073800 fatcat:yyhchydxabfsxjnvvfi7hsoexq

Domain-to-Domain Translation Model for Recommender System [article]

Linh Nguyen, Tsukasa Ishigaki
2018 arXiv   pre-print
It is based on generative adversarial networks (GANs), Variational Autoencoders (VAEs), and Cycle-Consistency (CC) for weight-sharing.  ...  Recently multi-domain recommender systems have received much attention from researchers because they can solve cold-start problem as well as support for cross-selling.  ...  Neural Network [12] is a state-of-the-art hybrid method for cross-domain recommender systems.  ... 
arXiv:1812.06229v1 fatcat:k56hol46dbhvthcwajdi25iov4

CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation [article]

Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan He
2020 arXiv   pre-print
Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data.  ...  Correspondingly, we derive a fast reinforced training algorithm of our framework to boost the sampler performance and sampler-recommender collaboration.  ...  Thus, collaborative sampler will be more suitable for the modern recommendation system with large item space. Effect of integrated recommendation.  ... 
arXiv:2011.07739v1 fatcat:wktdowkk7be2dnou63g2pzoslq

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [article]

Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
2020 arXiv   pre-print
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.  ...  This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that  ...  The generative learning for recommender systems is majorly using generative models, e.g. variational autoencoders (VAE) and generative adversarial neural network (GAN), for producing the potential ratings  ... 
arXiv:2004.03774v2 fatcat:q7mehir7hbbzpemw3q5fkby5ty

Research on Recommendation Systems using Deep Learning Models

2019 International journal of recent technology and engineering  
Due to this scenario, recommender systems that can recommend items appropriate for user's interest and likings have become mandatory.  ...  Deep Learning is used to generate recommendations and the research challenges specific to recommendation systems when using Deep Learning are also presented.  ...  Generative Adversarial Network-Based Collaborative Filtering Method Da'u, A et al., [16] represented Generative Adversarial Network GAN is a newlyy developed method which has two neural networks i) Generative  ... 
doi:10.35940/ijrte.d4609.118419 fatcat:wvnmghk64zgltgutejx5bmolty

Adversarial Collaborative Auto-encoder for Top-N Recommendation [article]

Feng Yuan The University of New South Wales)
2018 arXiv   pre-print
In this work, to address the above issue, we propose a general adversial training framework for neural network-based recommendation models, which improves both the model robustness and the overall performance  ...  Through simple modifications, our adversarial training framework can be applied to a host of neural network-based models whose robustness and performance are expected to be both enhanced.  ...  the impacts of each noise source, and propose a generic loss function for adversarial neural network models (ANN).  ... 
arXiv:1808.05361v1 fatcat:xrqviwrsdfeknea4jqd3iduqyu

From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems [article]

Yao Zhou, Haonan Wang, Jingrui He, Haixun Wang
2021 arXiv   pre-print
recommender systems without sacrificing their performance.  ...  With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications.  ...  Adversarial augmentation network. Considering that we aim to use adversarial perturbation for generating the explainable perturbations, the first step is to learn the base recommendation model 2 .  ... 
arXiv:2110.14844v1 fatcat:e3s7nbxivzhknidbckjek2hx2e

Collaborative Filtering Recommendation Algorithm Based on Attention GRU and Adversarial Learning

Hongbin Xia, Jingjing Li, Yuan Liu
2020 IEEE Access  
Therefore, the recommendation system is a necessary tool to help users obtain effective information.  ...  Building upon adversarial learning techniques [22] - [25] , our approach injects adversarial perturbations to the model parameters based on neural networks in matrix factorization recommendations.  ...  His research interest includes computer network optimization, natural language processing and computational intelligence.  ... 
doi:10.1109/access.2020.3038770 fatcat:uiiwv4qjvbcazn46o6et6cfguq

Enhancing Social Recommendation with Adversarial Graph Convolutional Networks [article]

Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, Lizhen Cui
2020 arXiv   pre-print
In this paper we propose a deep adversarial framework based on graph convolutional networks (GCN) to address these problems.  ...  Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data.  ...  Adversarial Training in Recommender Systems Generative adversarial networks (GANs) [23] have led a revolution in many fields including recommender systems.  ... 
arXiv:2004.02340v4 fatcat:2fg4c4c3kbdvnhu4rf32ssrglu
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