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.Full text not available from this repository.
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:||Cultural Communication and Computing Research Institute > Communication and Computing Research Centre|
|Depositing User:||Martin Beer|
|Date Deposited:||11 Feb 2014 14:31|
|Last Modified:||09 Nov 2016 16:50|
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