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Gender and Interest Targeting for Sponsored Post Advertising at Tumblr

Content Provider	ACM Digital Library
Author	Grbovic, Mihajlo Nagarajan, Ananth Radosavljevic, Vladan Djuric, Nemanja Bhamidipati, Narayan
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 can promote that content by sponsoring it to appear as an advertisement in the users' live feeds. In this paper, we present a framework that enabled two of the key targeted advertising components for Tumblr, gender and interest targeting. We describe the main challenges encountered during the development of the framework, which include the creation of a ground truth for training gender prediction models, as well as mapping Tumblr content to a predefined 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 a very limited amount of categorized keywords, and was shown to have superior performance over the baseline approaches. We successfully deployed gender and interest targeting capability in Yahoo production systems, delivering inference for users that covers more than 90% of daily activities on Tumblr. Online performance results indicate advantages of the proposed approach, where we observed 20% increase in user engagement with sponsored posts in comparison to untargeted campaigns.
Starting Page	1819
Ending Page	1828
Page Count	10
File Format	PDF MP4
ISBN	9781450336642
DOI	10.1145/2783258.2788616
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Audience modeling Computational advertising Data mining
Content Type	Audio Text
Resource Type	Article

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