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Filters
Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks
2019
IEEE transactions on multimedia
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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]
2021
arXiv
pre-print
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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]
2022
arXiv
pre-print
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2022 pre-print arXiv:2201.10711v2 fatcat:5evwlpbsbzajxokuloeivta5qeOther Versions
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]
2020
arXiv
pre-print
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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
2021
IEEE Transactions on Network Science and Engineering
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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]
2020
arXiv
pre-print
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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
2021
IEEE Internet of Things Journal
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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]
2018
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
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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]
2018
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2020
IEEE Access
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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]
2020
arXiv
pre-print
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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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