This paper explores an important and relatively unstudied quality measure of a sponsored search advertisement: bounce rate. The bounce rate of an ad can be informally defined as the fraction of users who click on the ad but almost immediately move on to other tasks. A high bounce rate can lead to poor advertiser return on investment, and suggests search engine users may be having a poor experience following the click. In this paper, we first provide quantitative analysis showing that bounce rate is an effective measure of user satisfaction. We then address the question, can we predict bounce rate by analyzing the features of the advertisement? An affirmative answer would allow advertisers and search engines to predict the effectiveness and quality of advertisements before they are shown. We propose solutions to this problem involving large-scale learning methods that leverage features drawn from ad creatives in addition

Web textual advertising can be interpreted as a search problem over the corpus of ads available for display in a particular context. In contrast to conventional information retrieval systems, which always return results if the corpus contains any documents lexically related to the query, in Web advertising it is acceptable, and occasionally even desirable, not to show any results. When no ads are relevant to the user’s interests, then showing irrelevant ads should be avoided since they annoy the user and produce no economic benefit. In this paper we pose a decision problem “whether to swing”, that is, whether or not to show any of the ads for the incoming request. We propose two methods for addressing this problem, a simple thresholding approach and a machine learning approach, which collectively analyzes the set of candidate ads augmented with external knowledge. Our experimental evaluation, based on over 28,000 editorial judgments, shows that we are able to predict, with high accuracy, when to“swing”for both content match and sponsored search advertising.

This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites, for the purpose of finding good audiences for brand advertising (as opposed to click maximizing, for example). Targeting social-network neighbors resonates well with advertisers, and on-line browsing behavior data counterintuitively can allow the identification of good audiences anonymously. Besides being one of the first papers to our knowledge on data mining for on-line brand advertising, this paper makes several important contributions. We introduce a framework for evaluating brand audiences, in analogy to predictive-modeling holdout evaluation. We introduce methods for extracting quasi-social networks from data on visitations to social networking pages, without collecting any information on the identities of the browsers or the content of the social-network pages. We introduce measures of brand proximity in the network, and show that audiences with high brand proximity indeed show substantially higher brand affinity. Finally, we provide evidence that the quasi-social network embeds a true social network, which along with results from social theory offers one explanation for the increases in audience brand affinity.

An improved understanding of the relationship between search intent, result quality, and searcher behavior is crucial for improving the effectiveness of web search. While recent progress in user behavior mining has been largely focused on aggregate server-side click logs, we present a new class of search behavior models that also exploit fine-grained user interactions with the search results. We show that mining these interactions, such as mouse movements and scrolling, can enable more effective detection of the user’s search goals. Potential applications include automatic search evaluation, improving search ranking, result presentation, and search advertising. We describe extensive experimental evaluation over both controlled user studies, and logs of interaction data collected from hundreds of real users. The results show that our method is more effective than the current state-of-the-art techniques, both for detection of searcher goals, and for an important practical application of predicting ad clicks for a given search session.

Web search results often integrate content from specialized corpora known as verticals. Given a query, one important aspect of aggregated search is the selection of relevant verticals from a set of candidate verticals. One drawback to previous approaches to vertical selection is that methods have not explicitly modeled user feedback. However, production search systems often record a variety of feedback information. In this paper, we present algorithms for vertical selection which adapt to user feedback. We evaluate algorithms using a novel simulator which models performance of a vertical selector situated in realistic query traffic.

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Three methods are proposed to classify queries by intent (CQI), e.g., navigational, informational, commercial, etc. Following mixed-initiative dialog systems, search engines should distinguish navigational queries where the user is taking the initiative from other queries where there are more opportunities for system initiatives (e.g., suggestions, ads). The query intent problem has a number of useful applications for search engines, affecting how many (if any) advertisements to display, which results to return, and how to arrange the results page. Click logs are used as a substitute for annotation. Clicks on ads are evidence for commercial intent; other types of clicks are evidence for other intents. We start with a simple Naïve Bayes baseline that works well when there is plenty of training data. When training data is less plentiful, we back off to nearby URLs in a click graph, using a method similar to Word-Sense Disambiguation. Thus, we can infer that designer trench is commercial because it is close to www.saksfifthavenue.com, which is known to be commercial. The baseline method was designed for precision and the backoff method was designed for recall. Both methods are fast and do not require crawling webpages. We recommend a third method, a hybrid of the two, that does no harm when there is plenty of training data, and generalizes better when there isn’t, as a strong baseline for the CQI task. 1 Classify Queries By Intent (CQI) Determining query intent is an important problem for today’s search engines. Queries are short (consisting of 2.2 terms on average (Beitzel et al., 2004)) and contain ambiguous terms. Search engines need to derive what users want from this limited source of information. Users may be searching for a specific page, browsing for information, or trying to buy something. Guessing the correct intent is important for returning relevant items. Someone searching for designer trench is likely to be interested in results or ads for trench coats, while someone searching for world war I trench might be irritated by irrelevant clothing advertisements.

We adapt a probabilistic latent variable model, namely GaP (Gamma-Poisson) [6], to ad targeting in the contexts of sponsored search (SS) and behaviorally targeted (BT) display advertising. We also approach the important problem of ad positional bias by formulating a one-latent-dimension GaP factorization. Learning from click-through data is intrinsically large scale, even more so for ads. We scale up the algorithm to terabytes of real-world SS and BT data that contains hundreds of millions of users and hundreds of thousands of features, by leveraging the scalability characteristics of the algorithm and the inherent structure of the problem including data sparsity and locality. Specifically, we demonstrate two somewhat orthogonal philosophies of scaling algorithms to large-scale problems, through the SS and BT implementations, respectively. Finally, we report the experimental results using Yahoo’s vast datasets, and show that our approach substantially outperform the state-of-the-art methods in prediction accuracy. For BT in particular, the ROC area achieved by GaP is exceeding 0.95, while one prior approach using Poisson regression [11] yielded 0.83. For computational performance, we compare a single-node sparse implementation with a parallel implementation using Hadoop MapReduce, the results are counterintuitive yet quite interesting. We therefore provide insights into the underlying principles of large-scale learning. 1

We describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks on advertisements. We present a novel approach using translation models to learn user click propensity from sparse click logs. Our relevance predictions are then applied to multiple sponsored search applications in both offline editorial evaluations and live online user tests. The predicted relevance score is used to improve the quality of the search page in three areas: filtering low quality ads, more accurate ranking for ads, and optimized page placement of ads to reduce prominent placement of low relevance ads. We show significant gains across all three tasks.

Abstract—We present a method for modeling, and automatically inferring, the current interest of a user in search advertising. Our task is complementary to that of predicting ad relevance or commercial intent of a query in the aggregate, since the user intent may vary significantly for the same query. To achieve this goal, we develop a fine-grained user interaction model for inferring searcher receptiveness to advertising. We show that modeling the search context and behavior can significantly improve the accuracy of ad clickthrough prediction for the current user, compared to the existing state-of-the-art classification methods that do not model this additional sessionlevel contextual and interaction information. In particular, our experiments over thousands of search sessions from hundreds of real users demonstrate that our model is more effective at predicting ad clickthrough within the same search session. Our work has other potential applications, such as improving search interface design (e.g., varying the number or type of ads) based on user interest, and behavioral targeting (e.g., identifying users interested in immediate purchase). I.