Context-Aware Personalized Web Search Using Navigation History

Wiem Chebil, Mohammad O. Wedyan, Haiyan Lu and Omar Ghaleb Elshaweesh
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Wiem Chebil: MARS Research Laboratory, University of Sousse, Tunisia
Mohammad O. Wedyan: Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
Haiyan Lu: University of Technology Sydney, Australia
Omar Ghaleb Elshaweesh: University of Technology Sydney, Australia

International Journal on Semantic Web and Information Systems (IJSWIS), 2020, vol. 16, issue 2, 91-107

Abstract: It is highly desirable that web search engines know users well and provide just what the user needs. Although great effort has been devoted to achieve this dream, the commonly used web search engines still provide a “one-fit-all” results. One of the barriers is lack of an accurate representation of user search context that supports personalised web search. This article presents a method to represent user search context and incorporate this representation to produce personalised web search results based on Google search results. The key contributions are twofold: a method to build contextual user profiles using their browsing behaviour and the semantic knowledge represented in a domain ontology; and an algorithm to re-rank the original search results using these contextual user profiles. The effectiveness of proposed new techniques were evaluated through comparisons of cases with and without these techniques respectively and a promising result of 35% precision improvement is achieved.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jswis0:v:16:y:2020:i:2:p:91-107

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