Enhancing the degree of personalization through Vector Space Model and Profile Ontology

AL SHARJI, Safiya, BEER, Martin and URUCHURTU, Elizabeth (2013). Enhancing the degree of personalization through Vector Space Model and Profile Ontology. In: THUY, Nguyen Thanh, OGAWA, Mizuhito, HO, Tu Bao, PIURI, Vincenzo and TRAN, Xuan-Tu, (eds.) Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2013 IEEE RIVF International Conference on. IEEE Xplore, 248-252.

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...
Link to published version:: https://doi.org/10.1109/RIVF.2013.6719902


Web browsers need to match the users' queries to the information available data bases. However, matching the users' needs with their interests and preferences to provide personalized search results in a ranked order of relevance entails a complex interaction of information attributes and as such, it remains one of the main challenges researchers face. Information Retrieval (IR) techniques focusing specifically on using Vector Space Model (VSM) with Profile Ontology (PO) hybridizationproved an improvement on personalized search results. We improve the degree of personalization by incorporating a new metric, the Dwell Time of each search session to optimize a learned re-ranked model. For a longitudinal naturalistic study of Web interactions, search logs were gathered as stimuli for the ranking algorithms of our personalized search engine. The performance of our re-ranking mechanism using Discounted Cumulative Gain (DCG) and F-measurewas tested. The scheme devised in this study was compared with the Google search engine. It was shown that, at the 10 top ranks of our personalized search engine, 14% improvement in the relevance is achieved.

Item Type: Book Section
Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
Identification Number: https://doi.org/10.1109/RIVF.2013.6719902
Page Range: 248-252
Depositing User: Martin Beer
Date Deposited: 11 Feb 2014 14:31
Last Modified: 18 Mar 2021 19:30
URI: https://shura.shu.ac.uk/id/eprint/7711

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