This book constitutes the refereed proceedings of the 30th annual European Conference on Information Retrieval Research, ECIR 2008, held in Glasgow, UK, in March/April 2008.

The 33 revised full papers and 19 revised short papers presented together with the abstracts of 3 invited lectures and 32 poster papers were carefully reviewed and selected from 139 full article submissions. The papers are organized in topical sections on evaluation, Web IR, social media, cross-lingual information retrieval, theory, video, representation, wikipedia and e-books, as well as expert search.

Every day, millions of users rely on search engines to satisfy the information needs required for performing many routine tasks. The effectiveness and efficiency of a search engine are two prime goals that form a natural trade-off. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency. This survey comprehensively reviews the foundations of search engines, from index layouts to basic query processing strategies, while also providing the latest trends in the literature in efficient query processing. It goes on to describe techniques in applying a cascading infrastructure within search systems, such as learned models obtained from learning-to-rank techniques. The survey also covers the selective application of query processing techniques to ensure that the required retrieval speed targets can be met. Finally, the authors bring the reader completely up-to-date by describing techniques for the efficient deployment of learned models in a multi-stage ranking system. Efficient Query Processing for Scalable Web Search will be a valuable reference for researchers and developers working on cutting edge of web system design where effective and efficient search is an integral part of the design.

Ranking in information retrieval has been traditionally approached as a pursuit of relevant information, under the assumption that the users' information needs are unambiguously conveyed by their submitted queries. Nevertheless, as an inherently limited representation of a more complex information need, every query can arguably be considered ambiguous to some extent. In order to tackle query ambiguity, search result diversification approaches have recently been proposed to produce rankings aimed to satisfy the multiple possible information needs underlying a query. This primer on the topic reviews the published literature on search result diversification. In particular, it discusses the motivations for diversifying the search results for an ambiguous query and provides a formal definition of the search result diversification problem. In addition, it describes the most successful approaches in the literature for producing and evaluating diversity in multiple search domains. Finally, it also discusses recent advances as well as open research directions in the field of search result diversification.