Online Social Network Sites have become a primary platform for brands and organizations to engage their audience by sharing image and video posts on their timelines. Different from traditional advertising, these posts are not restricted to the products or logo but include visual elements that express more in general the values and attributes of the brand, called brand associations. Since marketers are increasingly spending time in discovering and re-posting user generated posts that reflect the brand attributes, there is an increasing demand for such discovery systems. The goal of these systems is to assist brand experts in filtering through online collections of new user media to discover actionable posts, which match the brand value and have the potential to engage the consumers. Driven by this real-life application, we define and formulate a new task of content discovery for brands and propose a framework that learns to rank posts for brands from their historical timeline. We design a Personalized Content Discovery (PCD) framework to address the three challenges of high inter-brand similarity, sparsity of brand--post interactions, and diversification of timeline. To learn fine-grained brand representation and to generate explanations for the ranking, we automatically learn visual elements of posts from the timeline of brands and from a set of brand attributes in the domain of marketing. To test our framework we use two large-scale Instagram datasets that contain a total of more than 1.5 million image and video posts from the historical timeline of hundreds of brands from multiple verticals such as food and fashion. Extensive experiments indicate that our model can effectively learn fine-grained brand representations and outperform the closest state-of-the-art solutions.

中文翻译：

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学习图像的视觉元素以发现品牌帖子

在线社交网站已成为品牌和组织通过在其时间线上分享图像和视频帖子来吸引受众的主要平台。与传统广告不同，这些帖子不仅限于产品或标志，还包括更一般地表达品牌价值和属性的视觉元素，称为品牌联想。由于营销人员越来越多地花费时间来发现和重新发布反映品牌属性的用户生成的帖子，因此对这种发现系统的需求不断增加。这些系统的目标是帮助品牌专家通过新用户媒体的在线集合进行过滤，以发现与品牌价值相匹配并有可能吸引消费者的可操作帖子。在这个现实生活中的应用程序的驱动下，品牌内容发现并提出一个框架，学习根据品牌的历史时间线对帖子进行排名。我们设计了一个个性化内容发现（PCD）框架来解决品牌间高相似度、品牌稀疏性——帖子交互和时间线多样化的三个挑战。为了学习细粒度的品牌表示并为排名生成解释，我们从品牌时间线和营销领域的一组品牌属性中自动学习帖子的视觉元素。为了测试我们的框架，我们使用了两个大规模的 Instagram 数据集，其中包含来自食品和时尚等多个垂直领域的数百个品牌的历史时间线中总共超过 150 万条图像和视频帖子。