Ownership Recommendation via Iterative Adversarial Training

Paul, Agyemang; Zhao, Xin; Fang, Luping; Wu, Zhefu

doi:10.1007/s11063-021-10647-y

Cited by 3 publications

(8 citation statements)

References 46 publications

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“…Although the CPR model contributes to better recommendation performance, we believe that one potential disadvantage is that the entire system is prone to misoperation or malicious operation, which leads to recommendation error. As demonstrated in previous work [27]- [32], many cutting-edge recommendation systems are vulnerable to adversarial attacks. To maximize the loss function, He et al [27] compared the impact of adversarial attacks on model parameters using an adversarial personalized matrix factorization.…”

Section: Introductionmentioning

confidence: 82%

“…Most recently, He et al [27] demonstrated the weakness of Bayesian Personalized Matrix Factorization (BPR-MF) against adversarial perturbations obtained from the Fast Gradient Sign Method (FGSM) and suggested adversarial training technique as a defensive strategy. The proposed technique is tested and implemented in many algorithms for model robustness [28], [29], [32]. As we investigate enhancing the robustness of ownership recommendations, this line of research is relevant to this research.…”

Section: Adversarial Recommendationmentioning

confidence: 99%

“…As we investigate enhancing the robustness of ownership recommendations, this line of research is relevant to this research. ACPR [32] is an approach that applies to this study. The most significant distinctions between ACPR and ACPR-ADAM are as follows:…”

Section: Adversarial Recommendationmentioning

confidence: 99%

“…As a result, it improves local smoothness between similar attack examples, moving the algorithm closer to better generalization [33]. Recently, Paul et al [32] developed an ownership-based recommendation system that is defensible by using an iterative adversarial training approach on the ownership characteristics. However, while the iterative method is the most researched strategy, single-step adversarial training strategies that exhibited superior efficacy in recommendation systems [27]- [29] have been substantially under-examined in the ownership-based recommendation.…”

Section: Introductionmentioning

confidence: 99%

“…We propose Adversarial Consumption and Production Relationship (ACPR), a model that combines factorization machine [34] and single-step adversarial training for ownership recommendation. It enables us to take advantage of modeling consumption-production interactions with a factorization machine rather than the usual matrix factorization [32] while also boosting the robustness of the ownership recommender model. Furthermore, the maximal direction attack may cause the embedding vector of input x to be close to examples with different labels.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Directional Adversarial Training for Robust Ownership-Based Recommendation System

Paul

Cao

et al. 2022

IEEE Access

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Machine learning algorithms are susceptible to cyberattacks, posing security problems in computer vision, speech recognition, and recommendation systems. So far, researchers have made great strides in adopting adversarial training as a defensive strategy. Single-step adversarial training methods have been proposed as viable solutions for improving model generality and resilience. However, there has been little study to address this issue in the context of ownership-based recommendations, which may fail to capture stable results. In this work, we adapt the single-step adversarial training for ownership recommendation systems. Our main technical contributions are as follows: (1) We propose Adversarial Consumption and Production Relationship (ACPR), a model that combines factorization machine and single-step adversarial training for ownership recommendations. It enables us to take advantage of modeling consumption-production interactions with a factorization machine instead of the conventional matrix factorization method for ownership recommendations. (2) We enrich the ACPR technique with directional adversarial training and call our technique Adversarial Consumption and Production Relationship-Aware Directional Adversarial Model (ACPR-ADAM).The idea behind our ACPR-ADAM is that instead of the worst perturbation direction, the perturbation direction in the embedding space is restricted to other examples in the current embedding space, allowing us to incorporate the collaborative signal into the training process. Lastly, through extensive evaluations on Reddit and Pinterest, we demonstrate that our proposed method outperforms state-of-the-art methods. Compared with CPR and ACPR on Reddit and Pinterest datasets, our proposed ACPR-ADAM achieves 93%, 88%, and 72%, 69% enhancement in terms of AUC and HR, respectively.

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Section: Introductionmentioning

confidence: 82%