Multiple feedback based adversarial collaborative filtering with aesthetics

Visual-aware personalized recommendation systems can estimate the potential demand by evaluating consumer personalized preferences. In general, consumer feedback data is deduced from either explicit feedback or implicit feedback. However, explicit and implicit feedback raises the chance of malicious operation or misoperation, which can lead to deviations in recommended outcomes. Adversarial learning, a regularization approach that can resist disturbances, could be a promising choice for enhancing model resilience. We propose a novel adversarial collaborative filtering with aesthetics (ACFA) for the visual recommendation that utilizes adversarial learning to improve resilience and performance in the case of perturbation. The ACFA algorithm applies three types of input to the visual Bayesian personalized ranking: negative, unobserved, and positive feedback. Through feedbacks at various levels, it uses a probabilistic approach to obtain consumer personalized preferences. Since in visual recommendation, the aesthetic data in determining consumer preferences on product is critical, we construct the consumer personalized preferences model with aesthetic elements, and then use them to enhance the sampling quality when training the algorithm. To mitigate the negative effects of feedback noise, We use minimax adversarial learning to learn the ACFA objective function. Experiments using two datasets demonstrate that the ACFA model outperforms state-of-the-art algorithms on two metrics.