PhD Defence: Damir Vandic
In his dissertation ‘Intelligent Information Systems for Web Product Search’ ERIM’s Damir Vandic investigates how to design Web product search engines that automatically aggregate product information from different sources and allow users to perform effective and efficient queries on this data.
Damir defended his dissertation in the Senate Hall at Erasmus University Rotterdam on Thursday, 23 February 2017 at 13:30. His supervisor was Prof. Uzay Kaymak and his co-supervisor was Dr Flavius Frasincar. Other members of the Doctoral Committee were Prof. Franciska de Jong (University of Twente and Erasmus University Rotterdam), Prof. Martin Gaedke (Chemnitz University of Technology), Prof. Ricardo Baeza-Yates(University of Chile), Prof. Rommert Dekker (Erasmus University Rotterdam) and Prof. Trevor Martin (University of Bristol)
About Damir Vandic
Damir Vandic was born in Sarajevo, Bosnia and Herzegovina, on 30th of April 1987. He holds a cum laude B.Sc. degree and a cum laude M.Sc. degree in Economics and Informatics, obtained at Erasmus University Rotterdam, The Netherlands. His research interests cover areas such as machine learning, decision support systems, and Web information systems.
In October 2010, Damir obtained a NWO Mosaic scholarship and started his Ph.D. research under the auspices of the Erasmus Center for Business Intelligence (ECBI) at the Erasmus Research Institute of Management (ERIM), the Econometric Institute at the Erasmus School of Economics (ESE), and the Dutch Research School for Information and Knowledge Systems (SIKS). During his Ph.D. research, he went abroad to the United States, where he spent four months at Google as part of a research internship with the YouTube team in Mountain View, California.
Damir has published 27 peer-reviewed papers in the proceedings of prestigious international conferences, such as CAiSE, CIKM, DEXA, ESWC, and WWW. Additionally, Damir has published 8 articles in renowned journals such as Transactions on Knowledge and Data Engineering, Decision Support Systems, Expert Systems with Applications, and Journal of Web Engineering. He has also been active in the research community as a reviewer for journals such as Decision Support Systems, Expert Systems with Applications, and Information Systems.
Thesis Abstract
Over the last few years, online shopping has become very popular among consumers. However, this rapid growth of e-commerce has also introduced some issues. Users can get confused or are overwhelmed by the information they get presented while searching online for products. In an attempt to lighten this burden on consumers, several product search engines have been introduced that aggregate product descriptions and price information from the Web and allow the user to easily query this information. However, because it is difficult for systems to understand all the different ways online shops represent their production information, most product search engines expect to receive the data from the participating Web shops in a custom format. In this thesis, we investigate how to design Web product search engines that automatically aggregate product information from different sources and allow users to perform effective and efficient queries on this data. We first focus on how to classify products into an existing taxonomy using only their textual descriptions. Next, we focus on the problem of finding duplicates among product descriptions found on the Web. We also investigate how one can effectively populate ontologies from semi-structured product data using lexico-syntactic mappings and how to design an approach that automatically maps one product taxonomy into another using only the category names. Last, we perform two studies where we investigate how we can reduce the consumer search effort for product search engines that rely on faceted user interfaces.
Photos: Chris Gorzeman / Capital Images
