As one of the leading platforms for creative content, Tumblr offers advertisers a unique way of creating brand identity. Advertisers can tell their story through images, animation, text, music, video, and more, and promote that content by sponsoring it to appear as an advertisement in the streams of Tumblr users. In this paper we present a framework that enabled one of the key targeted advertising components for Tumblr, specifically gender and interest targeting. We describe the main challenges involved in development of the framework, which include creating the ground truth for training gender prediction models, as well as mapping Tumblr content to an interest taxonomy. For purposes of inferring user interests we propose a novel semi-supervised neural language model for categorization of Tumblr content (i.e., post tags and post keywords). The model was trained on a large-scale data set consisting of 6.8 billion user posts, with very limited amount of categorized keywords, and was shown to have superior performance over the bag-of-words model. We successfully deployed gender and interest targeting capability in Yahoo production systems, delivering inference for users that cover more than 90% of daily activities at Tumblr. Online performance results indicate advantages of the proposed approach, where we observed 20% lift in user engagement with sponsored posts as compared to untargeted campaigns.

本文介绍了一个为Tumblr平台开发的广告推广方法，该方法结合了性别和兴趣的定向广告功能，在弄清楚该开发框架面临的主要挑战（包括创建用于训练性别预测模型的基础事实数据以及将Tumblr内容映射到兴趣分类法）后，提出了一种新颖的半监督神经语言模型，用于分类Tumblr的内容。我们在超过68亿的用户帖子的大数据集上对该模型进行了训练，并在Yahoo的生产系统中成功部署了性别和兴趣定向功能，呈现出与未定向广告推广相比20%的用户关注度提升。

适用于 Tumblr 赞助帖广告的性别和兴趣定位