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.
One of the basic approaches in ad targeting is to target users with ads based on their demographics, such as age or gender. Historically, this approach has proven to work better than targeting random users. However, while for some products this type of targeting may be sufficient (e.g., women's makeup, women's clothing, man's razors, man's clothing), for others it is not effective enough and more involved profiling of users is required. A popular method in today's ad targeting that addresses this issue is known as interest targeting, where ads are assigned categories, such as sports'' or travel'', and machine learning models are trained to predict user interest in each of these categories using historical user behavior @cite_17 @cite_11 @cite_15 . Typically, a taxonomy is used to decide on the targeting categories, and for each ad category a separate predictive model is trained, able to estimate the probability of an ad click. Then, the models are evaluated on the entire user population, with @math users with the highest score selected for ad exposure. In this work we take this approach to develop a ad targeting platform on Tumblr.
@cite_15 @cite_11 @cite_17
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Determining the right audience for an advertising campaign is a well-established problem, of central importance to many Internet companies. Two distinct targeting approaches exist, the model-based approach, which leverages machine learning, and the rule-based approach, which relies on manual generation of targeting rules. Common rules include identifying users that had interactions (website visits, emails received, etc.) with the companies related to the advertiser, or search queries related to their product. We consider a problem of discovering such rules from data using Constrained Sparse PCA. The constraints are put in place to account for cases when evidence in data suggests a relation that is not appropriate for advertising. Experiments on real-world data indicate the potential of the proposed approach. User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users’ interest profiles via tracking their online behaviour and then delivers the relevant ads according to each user’s interest, which leads to higher targeting accuracy and thus more improved advertising performance. The current user profiling methods include building keywords and topic tags or mapping users onto a hierarchical taxonomy. However, to our knowledge, there is no previous work that explicitly investigates the user online visits similarity and incorporates such similarity into their ad response prediction. In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response. Technically, we propose a transfer learning model based on the probabilistic latent factor graphic models, where the users’ ad response profiles are generated from their online browsing profiles. The large-scale experiments based on real-world data demonstrate significant improvement of our solution over some strong baselines. To address the pressing need to provide transparency into the online targeted advertising ecosystem, we present AdReveal, a practical measurement and analysis framework, that provides a first look at the prevalence of different ad targeting mechanisms. We design and implement a browser based tool that provides detailed measurements of online display ads, and develop analysis techniques to characterize the contextual, behavioral and re-marketing based targeting mechanisms used by advertisers. Our analysis is based on a large dataset consisting of measurements from 103K webpages and 139K display ads. Our results show that advertisers frequently target users based on their online interests; almost half of the ad categories employ behavioral targeting. Ads related to Insurance, Real Estate and Travel and Tourism make extensive use of behavioral targeting. Furthermore, up to 65 of ad categories received by users are behaviorally targeted. Finally, our analysis of re-marketing shows that it is adopted by a wide range of websites and the most commonly targeted re-marketing based ads are from the Travel and Tourism and Shopping categories. In targeted display advertising, the goal is to identify the best opportunities to display a banner ad to an online user who is most likely to take a desired action such as purchasing a product or signing up for a newsletter. Finding the best ad impression, i.e., the opportunity to show an ad to a user, requires the ability to estimate the probability that the user who sees the ad on his or her browser will take an action, i.e., the user will convert. However, conversion probability estimation is a challenging task since there is extreme data sparsity across different data dimensions and the conversion event occurs rarely. In this paper, we present our approach to conversion rate estimation which relies on utilizing past performance observations along user, publisher and advertiser data hierarchies. More specifically, we model the conversion event at different select hierarchical levels with separate binomial distributions and estimate the distribution parameters individually. Then we demonstrate how we can combine these individual estimators using logistic regression to accurately identify conversion events. In our presentation, we also discuss main practical considerations such as data imbalance, missing data, and output probability calibration, which render this estimation problem more difficult but yet need solving for a real-world implementation of the approach. We provide results from real advertising campaigns to demonstrate the effectiveness of our proposed approach.