Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation

• Main Authors: CHEN, Xu, CHEN, Hanxiong, XU, Hongteng, ZHANG, Yongfeng, CAO, Yixin, QIN, Zheng, ZHA, Hongyuan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Databases and Information Systems; Graphics and Human Computer Interfaces; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/7463; https://ink.library.smu.edu.sg/context/sis_research/article/8466/viewcontent/3331184.3331254.pdf
• Institution: Singapore Management University

Fashion recommendation has attracted increasing attention from both industry and academic communities. This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information. Our basic intuition is that: for a fashion image, not all the regions are equally important for the users, i.e., people usually care about a few parts of the fashion image. To model such human sense, we learn an attention model over many pre-segmented image regions, based on which we can understand where a user is really interested in on the image, and correspondingly, represent the image in a more accurate manner. In addition, by discovering such fine-grained visual preference, we can visually explain a recommendation by highlighting some regions of its image. For better learning the attention model, we also introduce user review information as a weak supervision signal to collect more comprehensive user preference. In our final framework, the visual and textual features are seamlessly coupled by a multimodal attention network. Based on this architecture, we can not only provide accurate recommendation, but also can accompany each recommended item with novel visual explanations. We conduct extensive experiments to demonstrate the superiority of our proposed model in terms of Top-N recommendation, and also we build a collectively labeled dataset for evaluating our provided visual explanations in a quantitative manner.