By Vera Hollink, Jiyin He, Arjen de Vries (auth.), Ricardo Baeza-Yates, Arjen P. de Vries, Hugo Zaragoza, B. Barla Cambazoglu, Vanessa Murdock, Ronny Lempel, Fabrizio Silvestri (eds.)

This publication constitutes the court cases of the thirty fourth ecu convention on IR learn, ECIR 2012, held in Barcelona, Spain, in April 2012. The 37 complete papers, 28 poster papers and seven demonstrations awarded during this quantity have been rigorously reviewed and chosen from 167 submissions. The contributions are prepared in sections named: question illustration; blogs and online-community seek; semi-structured retrieval; overview; functions; retrieval versions; photograph and video retrieval; textual content and content material class, categorisation, clustering; structures potency; song; and posters.

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Extra info for Advances in Information Retrieval: 34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. Proceedings

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75%) the query was exactly the same. , at most only one character different. e. they have a Levenshtein distance <=1) then the messages clicked were also very similar. Queries that had a distance of >=2 had, however, very little click overlap. This is further evidence in favour of our assertion that high query overlap is an indicator of re-finding intent. Figure 1 shows query re-use over time. This is shown for equal queries (solid) and queries deemed similar due to them either having small Levenshtein distance (dashed) or one being a sub-string of the other (dotted).

C Springer-Verlag Berlin Heidelberg 2012 14 E. Bortnikov et al. Fig. 1. Google OneBox (left) and Yahoo! Direct Display (right) Fig. 2. Yahoo! g. in Web news search [4], leading to significant boost in click-through rates. Recently, Yahoo! Search incorporated transactional widgets, essentially opening a vendor-specific application in its search page upon queries of transactional nature corresponding to the vendor’s business. Figure 2 a Netflix widget, in response to a movie-name query. In order to support such an experience, one must associate a set of queries with each transactional widget.

They build a tripartite graph that is leveraged to estimate the probabilistic precision and recall of each template. Our framework estimates the perceived likelihood of a use-case through click-through information. Furthermore, the graph model in [1] requires a domain schema that specifies the attributes and corresponding instantiation vocabulary. It is not always easy to obtain such information, especially when the domain is complex and when there are too many attributes to consider. Our approach does not require the list of attributes known a priori.

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