论文标题
您可以向创意者而不是最终消费者推荐内容吗？基于用户首选的视觉样式的recsys
Can you recommend content to creatives instead of final consumers? A RecSys based on user's preferred visual styles
论文作者
论文摘要
由于用户不是最终的内容消费者，因此在内容市场中提供有意义的建议是具有挑战性的。取而代之的是，大多数用户是与他们从事工作的项目相关的兴趣，迅速和突然改变的项目。为了解决向内容创建者推荐图像的具有挑战性的任务，我们设计了一个recsys，以学习视觉样式的偏好，以横向用户使用的项目的语义。我们分析了任务的挑战与语义驱动的基于内容的建议，提出评估设置并在全球图像市场中解释其应用程序。 该技术报告是ACM Recsys '22介绍的论文“学习用户在图像市场中的首选视觉样式”的扩展。
Providing meaningful recommendations in a content marketplace is challenging due to the fact that users are not the final content consumers. Instead, most users are creatives whose interests, linked to the projects they work on, change rapidly and abruptly. To address the challenging task of recommending images to content creators, we design a RecSys that learns visual styles preferences transversal to the semantics of the projects users work on. We analyze the challenges of the task compared to content-based recommendations driven by semantics, propose an evaluation setup, and explain its applications in a global image marketplace. This technical report is an extension of the paper "Learning Users' Preferred Visual Styles in an Image Marketplace", presented at ACM RecSys '22.
