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Inferring Semantic Information from Websites: A View into Contextual Advertising and User Behavior Profiling Open Access


Other title
User Trace
User Behavior Profile
Type of item
Degree grantor
University of Alberta
Author or creator
Panwar, Abhimanyu
Supervisor and department
Miller, James (Electrical and Computer Engineering)
Examining committee member and department
Stroulia, Eleni(Computing Science)
Joseph, Dileepan (Electrical and Computer Engineering)
Miller, James (Electrical and Computer Engineering)
Department of Electrical and Computer Engineering
Software Engineering and Intelligent Systems
Date accepted
Graduation date
Master of Science
Degree level
The World Wide Web has become an important platform for the execution of diverse types of human endeavor. There are billions of webpages covering different subjects and users with varied backgrounds on the web. Every day, colossal volumes of data are collected about the usage of websites. Moreover the web is ever changing. Such situations present unique opportunities and problems for commercial organizations and researchers alike. In this thesis, we explore two prominent research problems concerning the web. The first problem is “delivering relevant ads to webpages based upon their content”. This practice is known as contextual advertising. Worldwide online advertisement revenues have reached US$117 billion. Contextual advertisement contributes to these revenues. The second problem is on “deducing user behavior patterns of a website”. Understanding user behavior on a website offers several advantages to web service providers, business managers and security experts. In this work, we present a novel two stage architecture for the ad-network to implement contextual advertising. An Ad-network has to deliver relevant ads to the requesting webpage in real time. It classifies the webpage based on its content into one of the nodes of the taxonomy and selects matching ads from the ad-repository. We present novel schemes for representing webpages by exploiting the semi-structured-ness of a webpage and its neighboring pages in the web graph, for the purpose of subject based classification. Initial experiments established the importance of a well-built taxonomy for this purpose. We construct a taxonomy, suitable for subject based webpage classification, from the Open Directory Project. Subsequently, we conducted comparative experiments on the Contextual Advertising systems implemented using the approaches described. We address the problem of mining user behavior patterns of a website. A user behavior profile (UBP) represents a sequence of webpages requested by the user to fulfill a purpose while browsing the website. To perform user behavior profiling of a website, we present an automated methodology to mine UBPs from the server log files of a website. We introduce an alphabet of 35 labels to represent functionality features implemented by sets of webpages. We also introduce 9 most common UBPs. We present an approach to prepare user traces, in the alphabet of labels, iii from the log files. We model a user trace as a Hidden Markov Model. Experiments reveal that the proposed technique performs better than other alternative algorithms. We present an industrial case study to prove the efficacy of the approach.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Panwar, Abhimanyu, Iosif-Viorel Onut, and James Miller. "Towards Real Time Contextual Advertising." Web Information Systems Engineering–WISE 2014. Springer International Publishing, 2014. 445-459

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