Adversarial Promotion for Video based Recommender Systems

Abstract

Short-form video content is fast to consume, easy to digest, and for most creators, inexpensive to make. Content Creators on Video Content platforms have a vested interest in having their videos appear as high as possible in recommendations that users are shown. This paper demonstrates how content creators can manipulate video content to adversarially promote their ranking in a recommendation model that uses action classification labels as an input feature. We focus on the context of these videos in terms of action classification to extract context about these videos to then rank and adversarially promote. Our attack successfully boosted the predicted like probability in 78 percent of generated lists for our model trained with non-perturbed inputs. However, after adversarial training, our model trained with perturbed inputs was 20 percent less effective in boosting the rank of targeted videos.