Proceedings Article10.1145/3298689.3346987
Adversarial tensor factorization for context-aware recommendation
Huiyuan Chen,Jing Li +1 more
- 10 Sep 2019
- pp 363-367
70
TL;DR: Tensor factorization and adversarial learning are combined for context-aware recommendations to reap the benefits of tensor factorization, while enhancing the robustness of a recommender model, and thus improves its eventual performance.
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Abstract: Contextual factors such as time, location, or tag, can affect user preferences for a particular item. Context-aware recommendations are thus critical to improve both quality and explainability of recommender systems, compared to traditional recommendations that are solely based on user-item interactions. Tensor factorization machines have achieved the state-of-the-art performance due to their capability of integrating users, items, and contextual factors in one unify way. However, few work has focused on the robustness of a context-aware recommender system. Improving the robustness of a tensor-based model is challenging due to the sparsity of the observed tensor and the multi-linear nature of tensor factorization. In this paper, we propose ATF, a model that combines tensor factorization and adversarial learning for context-aware recommendations. Doing so allows us to reap the benefits of tensor factorization, while enhancing the robustness of a recommender model, and thus improves its eventual performance. Empirical studies on two real-world datasets show that the proposed method outperforms standard tensor-based methods.
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Citations
A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks
TL;DR: The goal of this survey is to present recent advances on adversarial machine learning (AML) for the security of RS and to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions.
126
Recommender systems based on generative adversarial networks: A problem-driven perspective
TL;DR: A taxonomy of generative adversarial networks, along with their detailed descriptions and advantages is proposed, which elaborate on several open issues and current trends in GAN-based RSs.
Triple Adversarial Learning for Influence based Poisoning Attack in Recommender Systems
Chenwang Wu,Defu Lian,Yong Ge,Zhihao Zhu,Enhong Chen +4 more
- 14 Aug 2021
TL;DR: TrialAttack as mentioned in this paper proposes a triple adversarial learning for influence-based poisoning attack, which generates malicious users through adversarial training of the generator, discriminator, and influence module.
65
TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems
Tommaso Di Noia,Daniele Malitesta,Felice Antonio Merra +2 more
- 01 Jun 2020
TL;DR: This work proposes a novel adversarial attack approach, called Target Adversarial Attack against Multimedia Recommender Systems (TAaMR), to investigate the modification of MR behavior when the images of a category of low recommended products are perturbed to misclassify the deep neural classifier towards the class of more recommended products.
49
Recommender system based on temporal models: a systematic review
TL;DR: The results showed that time-dependent neighborhood models are the popularly used temporal models for DRS followed by the Time-dependent Matrix Factorization (TMF) and time-aware factors models, specifically Tensor models, respectively, and offline metrics such as precision and recalls are the most commonly used approaches to evaluate the performance of DRS.
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