Abstract
Knowing how easily pages within a website can be retrieved using the site’s search functionality provides crucial information to the site designer. If the system is not retrieving particular pages then the system or information may need to be changed to ensure that visitors to the site have the best chance of finding the relevant information. In this demo paper, we present a Page Retrievability Calculator, which estimates the retrievability of a page for a given search engine. To estimate the retrievability, instead of posing all possible queries, we focus on issuing only those likely to retrieve the page and use them to obtain an accurate approximation. We can also rank the queries associated with the page to show the site designer what queries are most likely to retrieve the pages and at what rank. With this application we can now explore how it might be possible to improve the site or content to improve the retrievability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Azzopardi, L., Owens, C.: Search engine predilection towards news media providers. In: Proc. of the 32nd ACM SIGIR, pp. 774–775 (2009)
Azzopardi, L., Vinay, V.: Retrievability: An evaluation measure for higher order information access tasks. In: Proc. of the 17th ACM CIKM, pp. 561–570 (2008)
Bashir, S., Rauber, A.: Improving retrievability of patents with cluster-based pseudo-relevance feedback documents selection. In: Proc. of the 18th ACM CIKM, pp. 1863–1866 (2009)
Bashir, S., Rauber, A.: Improving retrievability of patents in prior-art search. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 457–470. Springer, Heidelberg (2010)
Chi, E.H., Pirolli, P., Chen, K., Pitkow, J.: Using information scent to model user information needs and actions and the web. In: Proc. of the SIGCHI Conference, CHI 2001, pp. 490–497. ACM (2001)
Morville, P.: Ambient Findability. O’Reilly Media (2005)
Pickens, J., Cooper, M., Golovchinsky, G.: Reverted indexing for feedback and expansion. In: Proc. of the 19th ACM CIKM, pp. 1049–1058 (2010)
Wilkie, C., Azzopardi, L.: Relating retrievability, performance and length. In: Proc. of the 36th ACM SIGIR Conference, SIGIR 2013, pp. 937–940 (2013)
Zhang, Y., Zhu, H., Greenwood, S.: Web site complexity metrics for measuring navigability. In: Proc. of the 4th QSIC, pp. 172–179 (2004)
Zhou, Y., Leung, H., Winoto, P.: Mnav: A markov model-based web site navigability measure. IEEE Transactions on Software Engineering 33, 869–890 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Azzopardi, L., English, R., Wilkie, C., Maxwell, D. (2014). Page Retrievability Calculator. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_85
Download citation
DOI: https://doi.org/10.1007/978-3-319-06028-6_85
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06027-9
Online ISBN: 978-3-319-06028-6
eBook Packages: Computer ScienceComputer Science (R0)