Due to the advances in deep learning, visually-aware recommender systems (RS)
have recently attracted increased research interest. Such systems combine
collaborative signals with images, usually represented as feature vectors
outputted by pre-trained image models. Since item catalogs can be huge,
recommendation service providers often rely on images that are supplied by the
item providers. In this work, we show that relying on such external sources can
make an RS vulnerable to attacks, where the goal of the attacker is to unfairly
promote certain pushed items. Specifically, we demonstrate how a new visual
attack model can effectively influence the item scores and rankings in a
black-box approach, i.e., without knowing the parameters of the model. The main
underlying idea is to systematically create small human-imperceptible
perturbations of the pushed item image and to devise appropriate gradient
approximation methods to incrementally raise the pushed item's score.
Experimental evaluations on two datasets show that the novel attack model is
effective even when the contribution of the visual features to the overall
performance of the recommender system is modest.

该论文研究了基于图像的协同过滤推荐系统中可能存在的视觉攻击，并提出了一种新的视觉攻击模型，该模型可以有效地影响推荐系统中商品的排名，并证明了该攻击模型可以在不了解模型参数的情况下，通过对商品图像进行微小不可察觉的修改，从而提高商品的分数。

一种面向视觉感知推荐系统的黑盒攻击模型

A Black-Box Attack Model for Visually-Aware Recommender Systems

Visually-aware recommender systems use visual signals present in the
underlying data to model the visual characteristics of items and users'
preferences towards them. In the domain of clothing recommendation,
incorporating items' visual information (e.g., product images) is particularly
important since clothing item appearance is often a critical factor in
influencing the user's purchasing decisions. Current state-of-the-art
visually-aware recommender systems utilize image features extracted from
pre-trained deep convolutional neural networks, however these extremely
high-dimensional representations are difficult to interpret, especially in
relation to the relatively low number of visual properties that may guide
users' decisions.
In this paper we propose a novel approach to personalized clothing
recommendation that models the dynamics of individual users' visual
preferences. By using interpretable image representations generated with a
unique feature learning process, our model learns to explain users' prior
feedback in terms of their affinity towards specific visual attributes and
styles. Our approach achieves state-of-the-art performance on personalized
ranking tasks, and the incorporation of interpretable visual features allows
for powerful model introspection, which we demonstrate by using an interactive
recommendation algorithm and visualizing the rise and fall of fashion trends
over time.

本文提出了一种新颖的个性化服装推荐方法，该方法使用可解释的图像特征学习过程来解释用户对特定视觉属性和风格的偏好，并在个性化排名任务中实现了最先进的性能。