Gender and Interest Targeting for Sponsored Post Advertising at Tumblr

TL;DR

This paper introduces a novel semi-supervised neural language model for Tumblr content categorization, enabling effective gender and interest targeting in advertising, which significantly improves user engagement.

Contribution

It presents a new semi-supervised neural language model for interest inference from Tumblr content, addressing challenges in ground truth creation and interest taxonomy mapping.

Findings

Model outperforms bag-of-words baseline

Coverage of user inference exceeds 90% of daily activities

20% increase in user engagement with targeted ads

Abstract

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.