SPERM: sequential pairwise embedding recommendation with MI-FGSM

Visual recommendation systems have shown remarkable performance by leveraging consumer feedback and the visual attributes of products. However, recent concerns have arisen regarding the decline in recommendation quality when these systems are subjected to attacks that compromise the model parameters. While the fast gradient sign method (FGSM) and iterative FGSM (I-FGSM) are well-studied attack strategies, the momentum iterative FGSM (MI-FGSM), known for its superiority in the computer vision (CV) domain, has been overlooked. This oversight raises the possibility that visual recommender systems may be vulnerable to MI-FGSM, leading to significant vulnerabilities. Adversarial training, a regularization technique designed to withstand MI-FGSM attacks, could be a promising solution to bolster model resilience. In this research, we introduce MI-FGSM for visual recommendation. We propose the Sequential Pairwise Embedding Recommender with MI-FGSM (SPERM), a model that incorporates visual, temporal, and sequential information for visual recommendations through adversarial training. Specifically, we employ higher-order Markov chains to capture consumers’ sequential behaviors and utilize visual pairwise ranking to discern their visual preferences. To optimize the SPERM model, we employ a learning method based on AdaGrad. Moreover, we fortify the SPERM approach with adversarial training, where the primary objective is to train the model to withstand adversarial inputs introduced by MI-FGSM. Finally, we evaluate the effectiveness of our approach by conducting experiments on three Amazon datasets, comparing it with existing visual and adversarial recommendation algorithms. Our results demonstrate the efficacy of the proposed SPERM model in addressing adversarial attacks while enhancing visual recommendation performance.