Context-Aware Personalized Web Search Using Navigation History

Context-Aware Personalized Web Search Using Navigation History

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) and Omar Ghaleb Elshaweesh (University of Technology Sydney, Australia)
Copyright: © 2020 |Pages: 17
DOI: 10.4018/IJSWIS.2020040105

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.
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User profile modelling is necessary for any kind of personalization. A user profile is a digital representation of a particular user that defines the user’s preferences and interests. User data are collected either explicitly by obtaining feedback from the users or implicitly by monitoring user behavior when they are browsing the web. One of the most important representations of a user’s interests in a personalized retrieval system is the use of ontology, which is a promising solution for solving word ambiguity and the cold start problem (Baazaoui et al., 2008; Calegari & Pasi, 2010). Therefore, many studies have been conducted on context-aware computing in different fields such as mobile applications, recommender systems and information retrieval. In our research, we focus on how to represent user context in personalized web search.

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