Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction.

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Predicting clicks: estimating the click-through rate for new ads

We introduce the notion of query substitution, that is, generating a new query to replace a user's original search query. Our technique uses modifications based on typical substitutions web searchers make to their queries. In this way the new query is strongly related to the original query, containing terms closely related to all of the original terms. This contrasts with query expansion through pseudo-relevance feedback, which is costly and can lead to query drift. This also contrasts with query relaxation through boolean or TFIDF retrieval, which reduces the specificity of the query. We define a scale for evaluating query substitution, and show that our method performs well at generating new queries related to the original queries. We build a model for selecting between candidates, by using a number of features relating the query-candidate pair, and by fitting the model to human judgments of relevance of query suggestions. This further improves the quality of the candidates generated. Experiments show that our techniques significantly increase coverage and effectiveness in the setting of sponsored search.

Generating query substitutions

A system and method for automatically providing content associated with captured information is described. In some examples, the system receives input by a user, and automatically provides content or links to content associated with the input. In some examples, the system receives input via text entry or by capturing text from a rendered document, such as a printed document, an object, an audio stream, and so on.

Automatically providing content associated with captured information, such as information captured in real-time

Behavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia when looking at how much BT can truly help online advertising in commercial search engines. To answer this question, in this paper we provide an empirical study on the click-through log of advertisements collected from a commercial search engine. From the comprehensively experiment results on the sponsored search log of the commercial search engine over a period of seven days, we can draw three important conclusions: (1) Users who clicked the same ad will truly have similar behaviors on the Web; (2) Click-Through Rate (CTR) of an ad can be averagely improved as high as 670% by properly segmenting users for behavioral targeted advertising in a sponsored search; (3) Using the short term user behaviors to represent users is more effective than using the long term user behaviors for BT. The statistical t-test verifies that all conclusions drawn in the paper are statistically significant. To the best of our knowledge, this work is the first empirical study for BT on the click-through log of real world ads.

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How much can behavioral targeting help online advertising

This invention relates to an advertisement machine which provides advertisements to a user searching for desired information within a data network. The machine receives, from a user, a search request including a search argument corresponding to the desired information and searches, based upon the received search argument, a first database having data network related information to generate search results. It also correlates the received search argument to a particular advertisement in a second database having advertisement related information. The search results together with the particular advertisement are provided by the machine to the user.

Associative search engine

La recherche sponsorisee sur le Web, qui permet aux annonceurs de faire apparaitre leurs contenus dans les resultats affiches par un moteur de recherche, a demarre en 1998 avec GoTo, acquis par Yahoo! en 2002. Le prix de l'apparition sur un site donne peut etre fonction du nombre d'apparitions (cout pour mille), des clicks sur un lien (cout par click) ou des actes engendres (cout par action). La recherche payee souleve un interet grandissant au sein de la communaute academique.

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System and methods for identifying the potential advertising value of terms found on web pages

An example embodiment of the present invention provides processes for visually emphasizing the displayed URLs in query results based on implicit relevance feedback. In one process, the process identifies a web page which includes results returned by a search engine. Each result might include a displayed URL and an actual URL. The process determines whether the displayed URL matches any stored URLs which were included in previous results returned by the search engine and clicked through by the user. The process detects a click-through by matching the actual URL in an HTTP request emanating from a browser to an actual URL for a stored URL. The process visually emphasizes the displayed URL when presenting the web page to the user, if the displayed URL does not match any stored URL which has been clicked through and other factors indicate a probability the user will click through the displayed URL.

Visually Emphasizing Query Results Based on Relevance Feedback

Various embodiments are directed to a system and method providing associative matching of terms. Candidate terms are selected for building one or more associative matching models from one or more selected candidate sources. Associativity is defined to give editors the ability to label sample associative term pairs from the one or more candidate sources. The editors label sample candidate term pairs as being related. Features are determined that can differentiate associative from non-associative pairs. The selected features are used to build a model. The model is applied to determine whether a received query-candidate pair are associative.

Daniel C. Fain

Papers

Sponsored search: A brief history

System and methods for identifying the potential advertising value of terms found on web pages

Visually Emphasizing Query Results Based on Relevance Feedback

System and method for associative matching

Term expansion using associative matching of labeled term pairs