Showing papers by "Andrei Z. Broder published in 2011"

Presentation of Content Based on Utility

Abstract: Methods and systems for presenting content such as articles based on utility are provided. In one embodiment, a plurality of articles are determined, each article in the plurality of articles including article content and a corresponding preview icon, the preview icon defining a link to the corresponding article content when presented. For each article in the plurality of articles, a user experience utility value is determined. And for each article in the plurality of articles, an economic utility value is also determined. A ranked order of the articles is determined based upon each article's user experience utility value and economic utility value. And a portion of the preview icons of the articles are presented on a graphical display page in a priority orientation based on the ranked order of the articles.

Bid generation for advanced match in sponsored search

Abstract: Sponsored search is a three-way interaction between advertisers, users, and the search engine. The basic ad selection in sponsored search, lets the advertiser choose the exact queries where the ad is to be shown. To increase advertising volume, many advertisers opt into advanced match, where the search engine can select additional queries that are deemed relevant for the advertiser's ad. In advanced match, the search engine is effectively bidding on the behalf of the advertisers. While advanced match has been extensively studied in the literature from the ad relevance perspective there is little work that discusses how to infer the appropriate bid value for a given advanced match. The bid value is crucial as it affects both the ad placement in revenue reordering and the amount advertisers are charged in case of a click.We propose a statistical approach to solve the bid generation problem and examine two information sources: the bidding behavior of advertisers, and the conversion data. Our key finding suggests that sophisticated advertisers' bids are driven by many factors beyond clicks and immediate measurable conversions, likely capturing the value chain of an ad display ranging from views, clicks, profit margins, etc., representing the total ROI from the advertising.

The new frontier of web search technology: seven challenges

Abstract: The classic Web search experience, consisting of returning "ten blue links" in response to a short user query, is powered today by a mature technology where progress has become incremental and expensive. Furthermore, the "ten blue links" represent only a fractional part of the total Web search experience: today, what users expect and receive in response to a "web query" is a plethora of multi-media information extracted and synthesized from numerous sources on and off the Web. In consequence, we argue that the major technical challenges in Web search are now driven by the quest to satisfy the implicit and explicit needs of users, continuing a long evolutionary trend in commercial Web search engines going back more than fifteen years, moving from relevant document selection towards satisfactory task completion. We identify seven of these challenges and discuss them in some detail.

Web Page Summarization for Just-in-Time Contextual Advertising

Abstract: Contextual advertising is a type of Web advertising, which, given the URL of a Web page, aims to embed into the page the most relevant textual ads available. For static pages that are displayed repeatedly, the matching of ads can be based on prior analysis of their entire content; however, often ads need to be matched to new or dynamically created pages that cannot be processed ahead of time. Analyzing the entire content of such pages on-the-fly entails prohibitive communication and latency costs. To solve the three-horned dilemma of either low relevance or high latency or high load, we propose to use text summarization techniques paired with external knowledge (exogenous to the page) to craft short page summaries in real time.Empirical evaluation proves that matching ads on the basis of such summaries does not sacrifice relevance, and is competitive with matching based on the entire page content. Specifically, we found that analyzing a carefully selected 6p fraction of the page text can sacrifice only 1p--3p in ad relevance. Furthermore, our summaries are fully compatible with the standard JavaScript mechanisms used for ad placement: they can be produced at ad-display time by simple additions to the usual script, and they only add 500--600 bytes to the usual request. We also compared our summarization approach, which is based on structural properties of the HTML content of the page, with a more principled one based on one of the standard text summarization tools (MEAD), and found their performance to be comparable.

Multi-step captcha with serial time-consuming decryption of puzzles

Abstract: A system and method for implementing a multi-step challenge and response test includes steps or acts of: using an input/output subsystem for presenting a series of challenges to a user that require said user to correctly solve each challenge before a next challenge is revealed to the user; receiving the user's response to each challenge; and submitting a last response in the series of challenges to a server for validation. The method further includes: using a processor device configured to perform for each challenge in the series of challenges: internally validating the response by comparing the user's response to a correct response; and using the user's response, decrypting the next challenge to reveal the next challenge; wherein the next challenge remains obfuscated until a previous challenge is correctly solved.

Mining global email folders for identifying auto-folder tags

Abstract: Embodiments are directed towards identifying auto-folder tags for messages by using a combinational optimization approach of bi-clustering folder names and features of messages based on relationship strengths. The combinational optimization approach of bi-clustering, generally, groups a plurality of folder names and a plurality of features into one or more metafolders to optimize a cost. The cost is based on an aggregate of cut relationship strengths, where a cut results when a relationship folder name and feature are grouped in separate metafolders. Furthermore, the plurality of folder names and the plurality of features are obtained by monitoring actions of a plurality of users, where the folder names are user generated folder names and features are from a plurality of messages. The metafolders may be used to tag new user messages with an auto-folder tag.

Efficiently evaluating graph constraints in content-based publish/subscribe

Abstract: We introduce the problem of evaluating graph constraints in content-based publish/subscribe (pub/sub) systems. This problem formulation extends traditional content-based pub/sub systems in the following manner: publishers and subscribers are connected via a (logical) directed graph G with node and edge constraints, which limits the set of valid paths between them. Such graph constraints can be used to model a Web advertising exchange (where there may be restrictions on how advertising networks can connect advertisers and publishers) and content delivery problems in social networks (where there may be restrictions on how information can be shared via the social graph). In this context, we develop efficient algorithms for evaluating graph constraints over arbitrary directed graphs G. We also present experimental results that demonstrate the effectiveness and scalability of the proposed algorithms using a realistic dataset from Yahoo!'s Web advertising exchange.

Introduction to display advertising: a half-day tutorial

Abstract: Display advertising is one of the two major advertising channels on the web (in addition to search advertising). Display advertising on the Web is usually done by graphical ads placed on the publishers' Web pages. There is no explicit user query, and the ad selection is performed based on the page where the ad is placed (contextual targeting) or user's past activities (behavioral targeting). In both cases, sophisticated text analysis and learning algorithms are needed to provide relevant ads to the user. In this tutorial we will overview the display advertising marketplace, and technologies that power the display advertising platforms.

An introduction to online targeted advertising: principles, implementation, controversies

Abstract: Online user interaction is becoming increasingly personalized both via explicit means: customizations, options, add-ons, skins, apps, etc. and via implicit means, that is, deep data mining of user activities that allows automated selection of content and experiences, e.g. individualized top news stories, personalized ranking of search results, personal "radio stations" that capture idiosyncratic tastes from past choices, individually recommended purchases, and so on. On the other hand, the vast majority of providers of content and services (e.g. portals, search engines, social sites) are supported by advertising, which at core, is just a different type of information. Thus, not surprisingly, on-line advertising is becoming increasingly personalized as well, supported by an emerging new scientific sub-discipline, Computational Advertising. The central problem 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 communicating via instant-messaging or via e-mail, a user interacting with a portable device, and many more. 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 problem leads to a variety of massive optimization and search problems, with complicated constraints. The solution to these problems provides the scientific and technical foundations of the online advertising industry, which according to E-Marketer, is estimated to achieve $25.8B dollars in revenue in 2010 in US alone, for the first time exceeding print advertising revenue at "only" 22.8B dollars. The focus of this talk is targeted advertising, a form of personalized advertising whereby advertisers specify the features of their desired audience, either explicitly, by specifying characteristics such as demographics, location, and context, or implicitly by providing examples of their ideal audience. A particular form of targeted advertising is behavioral targeting, where the desired audience is characterized by its past behavior. We will discuss how targeted advertising fits the optimization framework above, present some of the mechanisms by which targeted and behavioral advertising are implemented, and briefly survey the controversies surrounding behavioral advertising as a potential infringement on user privacy. We will conclude with some speculations about the future of personalized advertising and interesting areas of research.

System and method for providing targeted applications within a search results page

Abstract: The present invention provides a method and system for providing targeted applications within a search results page. The method and system includes receiving a search query from a user and interpreting the search query. The method and system then first maps the interpreted query to one or more action templates, wherein mapping the interpreted query to one or more action templates comprises selecting one or more actions associated with the interpreted query. The method and system then maps the selected one or more actions associated with the interpreted query to a plurality of applications and selecting one or more applications associated with the one or more actions. Finally, the method and system displays the one or more applications within a search results page.