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 @math 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.
 Online service platforms (OSPs), such as search engines, news-websites, ad-providers, etc., serve highly personalized content to the user, based on the profile extracted from her history with the OSP. In this paper, we capture OSP's personalization for an user in a new data structure called the personalization vector (?), which is a weighted vector over a set of topics, and present efficient algorithms to learn it. Our approach treats OSPs as black-boxes, and extracts η by mining only their output, specifically, the personalized (for an user) and vanilla (without any user information) contents served, and the differences in these content. We believe that such treatment of OSPs is a unique aspect of our work, not just enabling access to (so far hidden) profiles in OSPs, but also providing a novel and practical approach for retrieving information from OSPs by mining differences in their outputs. We formulate a new model called Latent Topic Personalization (LTP) that captures the personalization vector in a learning framework and present efficient inference algorithms for determining it. We perform extensive experiments targeting search engine personalization, using data from both real Google users and synthetic setup. Our results indicate that LTP achieves high accuracy (R-pre = 84 ) in discovering personalized topics.For Google data, our qualitative results demonstrate that the topics determined by LTP for a user correspond well to his ad-categories determined by Google. Computational advertising is an emerging scientific discipline, at the intersection of large scale search and text analysis, information retrieval, statistical modeling, machine learning, optimization, and microeconomics. The central challenge of computational advertising is to find the "best match" between a given user in a given context and a suitable advertisement. The context could be a user entering a query in a search engine ("sponsored search"), a user reading a web page ("content match" and "display ads"), a user conversing on a cell phone ("mobile advertising"), and so on. The information about the user can vary from scarily detailed to practically nil. The number of potential advertisements might be in the billions. Thus, depending on the definition of "best match" this challenge leads to a variety of massive optimization and search problems, with complicated constraints. The main part of this talk will give an introduction to computational advertising and present some illustrative research. In the second part we will discuss connections to recommender systems and present a couple of open problems of potential interest to both communities.