Ad Retrieval Systems

Over the past decade, online advertising became the principal economic force behind many an Internet service, from major search engines to globe-spanning social networks to blogs. There is often a tension between online advertising and user experience, but on the other hand, advertising revenue enables a myriad of free Web services to the public and fosters a great deal of innovation. Matching the advertisers’ message to a receptive and interested audience benefits both sides; indeed, literally hundreds of millions of users occasionally click on the ads, hence they should be considered relevant to the users’ information needs by current IR evaluation principles. The utility of ads can be better explained by considering advertising as a medium of information [2,3]. Similarly to aggregated search [1], which enhances users’ Web search experience with relevant news, local results, user-generated content, or multimedia, online advertising provides another rich source of content. This source, however, is in a complexity class of its own, due to the brevity of bid phrases, ad text being optimized for presentation rather than indexing, and multiple, possibly contradictory utility functions. A new scientific sub-discipline—Computational Advertising—has recently emerged, which strives to make online advertising integral to the user experience and relevant to the users’ information needs, as well as economically worthwhile to the advertiser and the publisher. In this talk we discuss the unique algorithmic challenges posed by searching the ad corpus, and report on empirical evaluation of large-scale advertising systems in vivo. At first approximation, finding user-relevant ads is akin to ad hoc information retrieval, where the user context is distilled into a query executed against an index of ads. However, the elaborate structure of ad campaigns, along with the cornucopia of pertinent non-textual information, makes ad retrieval substantially and interestingly different. We show how to adapt standard IR methods for ad retrieval, by developing structure-aware indexing techniques and by augmenting the ad selection process with exogenous knowledge. Computational advertising also employs a host of NLP techniques, from text summarization for just-in-time ad matching, to machine translation for cross-language ad retrieval, to natural language generation for automatic construction of advertising campaigns. Last but not least, we study the interplay between the algorithmic and sponsored search results, as well as formulate and explore context transfer, which characterizes the user’s transition from Web search to the context of the landing page following an ad-click. These studies offer deep insights into how users interact with ads, and facilitate better understanding of the much broader notion of relevance in ad retrieval compared to Web search.