Nanas, Nikolaos; Kruschwitz, Udo; Albakour, M-Dyaa; Fasli, Maria; Song, Dawei; Kim, Yunhyong; Cerviño, Ulises and De Roeck, Anne
PDF (Version of Record)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (702Kb) | Preview
|Google Scholar:||Look up in Google Scholar|
Interactive information retrieval has received much attention in recent years, e.g. . Furthermore, increased activity in developing interactive features in search systems used across existing popular Web search engines suggests that interactive systems are being recognised as a promising next step in assisting information search. One of the most challenging problems with interactive systems however remains evaluation.
We describe the general specifications of a methodology for conducting controlled and reproducible experiments in the context of interactive search. It was developed in the AutoAdapt project1 focusing on search in intranets, but the methodology is more generic than that and can be applied to interactive Web search as well. The goal of this methodology is to evaluate the ability of different algorithms to produce domain models that provide accurate suggestions for query modifications. The AutoAdapt project investigates the application of automatically constructed adaptive domain models for providing suggestions for query modifications to the users of an intranet search engine. This goes beyond static models such as the one employed to guide users who search the Web site of the University of Essex which is based on a domain model that has been built in advance using the documents’ markup structure.
|Item Type:||Conference Item|
|Copyright Holders:||2010 The Authors|
|Academic Unit/Department:||Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
Other Departments > Vice-Chancellor's Office
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Dawei Song|
|Date Deposited:||01 Nov 2012 11:52|
|Last Modified:||25 Feb 2016 10:58|
|Share this page:|
► Automated document suggestions from open access sources
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.