  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.
