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.
We consider personalization through the domain of ad targeting @cite_16 , where the task is to find best matching ads to be displayed for each individual user. This improves user's online experience (as only relevant and interesting ads are shown) and can lead to increased revenue for the advertisers (as users are more likely to click on the ad and make a purchase). Due to its large impact and many open research questions, targeted advertising has garnered significant interest from machine learning community, as witnessed by a large number of recent workshops www.targetad-workshop.net . and publications @cite_12 @cite_7 .
@cite_16 @cite_7 @cite_12
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Internet display advertising is a critical revenue source for publishers and online content providers, and is supported by massive amounts of user and publisher data. Targeting display ads can be improved substantially with machine learning methods, but building many models on massive data becomes prohibitively expensive computationally. This paper presents a combination of strategies, deployed by the online advertising firm Dstillery, for learning many models from extremely high-dimensional data efficiently and without human intervention. This combination includes: (i) A method for simple-yet-effective transfer learning where a model learned from data that is relatively abundant and cheap is taken as a prior for Bayesian logistic regression trained with stochastic gradient descent (SGD) from the more expensive target data. (ii) A new update rule for automatic learning rate adaptation, to support learning from sparse, high-dimensional data, as well as the integration with adaptive regularization. We present an experimental analysis across 100 different ad campaigns, showing that the transfer learning indeed improves performance across a large number of them, especially at the start of the campaigns. The combined "hands-free" method needs no fiddling with the SGD learning rate, and we show that it is just as effective as using expensive grid search to set the regularization parameter for each campaign. 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. Online advertising has become a key source of revenue for both web search engines and online publishers. For them, the ability of allocating right ads to right webpages is critical because any mismatched ads would not only harm web users' satisfactions but also lower the ad income. In this paper, we study how online publishers could optimally select ads to maximize their ad incomes over time. The conventional offline, content-based matching between webpages and ads is a fine start but cannot solve the problem completely because good matching does not necessarily lead to good payoff. Moreover, with the limited display impressions, we need to balance the need of selecting ads to learn true ad payoffs (exploration) with that of allocating ads to generate high immediate payoffs based on the current belief (exploitation). In this paper, we address the problem by employing Partially observable Markov decision processes (POMDPs) and discuss how to utilize the correlation of ads to improve the efficiency of the exploration and increase ad incomes in a long run. Our mathematical derivation shows that the belief states of correlated ads can be naturally updated using a formula similar to collaborative filtering. To test our model, a real world ad dataset from a major search engine is collected and categorized. Experimenting over the data, we provide an analyse of the effect of the underlying parameters, and demonstrate that our algorithms significantly outperform other strong baselines. 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.