No abstract

Can machine learning models be easily fooled? Despite the recent surge of interest in learned adversarial attacks in other domains, in the context of recommendation systems this question has mainly been answered using hand-engineered fake user profles. This paper attempts to reduce this gap. We provide a formulation for learning to attack a recommender as a repeated general-sum game between two players, i.e., an adversary and a recommender oblivious to the adversary's existence. We consider the challenging case of poisoning attacks, which focus on the training phase of the recommender model. We generate adversarial user profles targeting subsets of users or items, or generally the top-K recommendation quality. Moreover, we ensure that the adversarial user profles remain unnoticeable by preserving proximity of the real user rating/interaction distribution to the adversarial fake user distribution. To cope with the challenge of the adversary not having access to the gradient of the recommender's objective with respect to the fake user profles, we provide a non-trivial algorithm building upon zero-order optimization techniques. We ofer a wide range of experiments, instantiating the proposed method for the case of the classic popular approach of a low-rank recommender, and illustrating the extent of the recommender's vulnerability to a variety of adversarial intents. These results can serve as a motivating point for more research into recommender defense strategies against machine learned attacks. CCS CONCEPTS • Information systems → Recommender systems.

The goal of collaborative filtering is to get accurate recommendations at the top of the list for a set of users. From such a perspective, collaborative ranking based formulations with suitable ranking loss functions are natural. While recent literature has explored the idea based on objective functions such as NDCG or Average Precision, such objectives are difficult to optimize directly. In this paper, building on recent advances from the learning to rank literature, we introduce a novel family of collaborative ranking algorithms which focus on accuracy at the top of the list for each user while learning the ranking functions collaboratively. We consider three specific formulations, based on collaborative p-norm push, infinite push, and reverse-height push, and propose e cient optimization methods for learning these models. Experimental results illustrate the value of collaborative ranking, and show that the proposed methods are competitive, usually better than existing popular approaches to personalized recommendation.