Abstract
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.
Highlights
In an overloaded digital environment, recommendation systems are the most commonly used concepts for implementing personalization systems to provide information services to users
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
The existing maximal direction technique cannot keep the original semantic information in consumption-production relationships. We address this issue by imposing appropriate constraints on the perturbation direction using directional adversarial training and term the method Adversarial Consumption and Production Relationship-Aware Directional Adversarial Model (ACPR-ADAM)
Summary
In an overloaded digital environment, recommendation systems are the most commonly used concepts for implementing personalization systems to provide information services to users. Recommendation systems consist of different recommendation paradigms based on the information available to generate recommendations. Demographic methods were the most relevant approaches because of the availability of such information. In addition to previous concepts, some studies have referred to other concepts such as social, knowledge-based, or hybrid filtering [15] depending on the methodology and information used for recommendation generation. Model-based collaborative filtering approaches are prominent in the recommendation system community and highly desired in industrial applications due to their interpretability and effectiveness. The fundamental tenet is that a consumer may prefer items similar to those with which he has previously interacted. The similarity is predicted based on previous interactions between users (see figure 1). Model-based collaborative filtering methods primarily used linear approaches to construct their models. Matrix factorization [16]–[18], which transforms consumers and products into a low-dimensional shared space and represents the
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