Vertical selection in the information domain of children
| Autor: | Djoerd Hiemstra, Theo Huibers, Sergio Duarte Torres |
|---|---|
| Přispěvatelé: | Databases (Former) |
| Rok vydání: | 2013 |
| Předmět: |
Computer science
Aggregated search CR-H.3.3 02 engineering and technology computer.software_genre IR-88361 Domain (software engineering) Set (abstract data type) 020204 information systems 0202 electrical engineering electronic engineering information engineering Selection (linguistics) vertical selection Representation (mathematics) Evaluation Children Information retrieval EWI-23993 Large sample Information domain METIS-300171 020201 artificial intelligence & image processing Selection method Data mining computer Social Media MathematicsofComputing_DISCRETEMATHEMATICS |
| Zdroj: | JCDL Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, JDCL 2013, 57-66 STARTPAGE=57;ENDPAGE=66;TITLE=Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, JDCL 2013 |
| DOI: | 10.1145/2467696.2467714 |
| Popis: | In this paper we explore the vertical selection methods in aggregated search in the specific domain of topics for children between 7 and 12 years old. A test collection consisting of 25 verticals, 3.8K queries and relevant assessments for a large sample of these queries mapping relevant verticals to queries was built. We gather relevant assessment by envisaging two aggregated search systems: one in which the Web vertical is always displayed and in which each vertical is assessed independently from the web vertical. We show that both approaches lead to a different set of relevant verticals and that the former is prone to bias of visually oriented verticals. In the second part of this paper we estimate the size of the verticals for the target domain. We show that employing the global size and domain specific size estimation of the verticals lead to significant improvements when using state-of-the art methods of vertical selection. We also introduce a novel vertical and query representation based on tags from social media and we show that its use lead to significant performance gains. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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