Hugo Zaragoza

Ricardo Baeza-Yates

Arjen P. de Vries

B. Barla Cambazoglu

Vanessa Murdock

Ronny Lempel

Fabrizio Silvestri

This book constitutes the proceedings of the 34th European Conference on IR Research, ECIR 2012, held in Barcelona, Spain, in April 2012. The 37 full papers, 28 poster papers and 7 demonstrations presented in this volume were carefully reviewed and selected from 167 submissions. The contributions are organized in sections named: query representation; blogs and online-community search; semi-structured retrieval; evaluation; applications; retrieval models; image and video retrieval; text and content classification, categorisation, clustering; systems efficiency; industry track; and posters.

34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012, Proceedings

Advances in Information Retrieval

Stephen Robertson

The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970-80s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account structure and link-graph information. Again, this has led to one of the most successful web-search and corporate-search algorithms, BM25F. The Probabilistic Relevance Framework: BM25 and Beyond presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25, BM25F. Besides presenting a full derivation of the PRF ranking algorithms, it provides many insights about document retrieval in general, and points to many open challenges in this area. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimization for models with free parameters. The Probabilistic Relevance Framework: BM25 and Beyond is self-contained and accessible to anyone with basic knowledge of probability and inference

BM25 and Beyond

The Probabilistic Relevance Framework

Hugo Zaragoza

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