Images account for a significant part of user decisions in many application
scenarios, such as product images in e-commerce, or user image posts in social
networks. It is intuitive that user preferences on the visual patterns of image
(e.g., hue, texture, color, etc) can be highly personalized, and this provides
us with highly discriminative features to make personalized recommendations.
Previous work that takes advantage of images for recommendation usually
transforms the images into latent representation vectors, which are adopted by
a recommendation component to assist personalized user/item profiling and
recommendation. However, such vectors are hardly useful in terms of providing
visual explanations to users about why a particular item is recommended, and
thus weakens the explainability of recommendation systems.
As a step towards explainable recommendation models, we propose visually
explainable recommendation based on attentive neural networks to model the user
attention on images, under the supervision of both implicit feedback and
textual reviews. By this, we can not only provide recommendation results to the
users, but also tell the users why an item is recommended by providing
intuitive visual highlights in a personalized manner. Experimental results show
that our models are not only able to improve the recommendation performance,
but also can provide persuasive visual explanations for the users to take the
recommendations.

本文提出了基于注意力神经网络的可视化解释推荐模型，这种模型可同时提供用户感兴趣的推荐结果和推荐理由，其关键在于模拟用户注意力，使用隐式反馈和文本评价进行监督学习。实验结果表明，所提出的模型不仅可提高推荐性能，还可以提供具有说服力的视觉解释。