Abstract
Commercial information access providers increasingly incorporate content from a large number of specialized services created for particular information-seeking tasks. For example, an aggregated Web search page may include results from image databases and news collections in addition to the traditional Web search results; a news provider may dynamically arrange related articles, photos, comments, or videos on a given article page. These auxiliary services, known as verticals, include search engines that focus on a particular domain (e.g., news, travel, sports), search engines that focus on a particular type of media (e.g., images, video, audio), and APIs to highly targeted information (e.g., weather forecasts, map directions, or stock prices). The goal of aggregated search is to provide integrated access to all verticals within a single information context. Although aggregated search is related to classic work in distributed information retrieval, it has unique signals, techniques, and evaluation methods in the context of the Web and other production information access systems. In this chapter, we present the core problems associated with aggregated search, which include sources of predictive evidence, relevance modeling, and evaluation.
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