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User behaviour modeling and content based speculative web page retrieval
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. (Digital Media Laboratory)
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. (Digital Media Laboratory)
2006 (English)In: Data & Knowledge Engineering, ISSN 0169-023X, Vol. 59, no 3, 770-788 p.Article in journal (Refereed) Published
Abstract [en]

This paper provides a transparent and speculative algorithm for content based web page prefetching. The algorithm relies on a profile based on the Internet browsing habits of the user. It aims at reducing the perceived latency when the user requests a document by clicking on a hyperlink. The proposed user profile relies on the frequency of occurrence for selected elements forming the web pages visited by the user. These frequencies are employed in a mechanism for the prediction of the user’s future actions. For the anticipation of an adjacent action, the anchored text around each of the outbound links is used and weights are assigned to these links. Some of the linked documents are then prefetched and stored in a local cache according to the assigned weights. The proposed algorithm was tested against three different prefetching algorithms and yield improved cache–hit rates given a moderate bandwidth overhead. Furthermore, the precision of accurately inferring the user’s preference is evaluated through the recall–precision curves. Statistical evaluation testifies that the achieved recall–precision performance improvement is significant.

Place, publisher, year, edition, pages
Amsterdam: Elsevier Science Publishers B. V. , 2006. Vol. 59, no 3, 770-788 p.
Keyword [en]
link prefetching, user behavior modeling, bigrams
Identifiers
URN: urn:nbn:se:umu:diva-19210DOI: 10.1016/j.datak.2005.11.005OAI: oai:DiVA.org:umu-19210DiVA: diva2:201570
Available from: 2009-03-05 Created: 2009-03-05 Last updated: 2011-04-28Bibliographically approved

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Publisher's full texthttp://portal.acm.org/citation.cfm?id=1228384

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Li, Haibo

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  • de-DE
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