During the last few years the Internet and the WWW have become a major source of information as well as an essential platform for mass media, communication, e-commerce and entertainment. This expansion has led to information overload so finding or searching for relevant information has become more and more challenging. Personalization and recommender systems have been widely used during the past few years to overcome this information overload problem. The main objective of these systems is to learn user interests and then provide a personalized experience to each user accordingly. However; as information on the WWVV increases, so do users' demands: web personalization systems need to provide users not only with recommendations for relevant information, but also provide these recommendations in the right situation. However, when examining the current works in the personalization field, we can see that there is a limitation in providing a generic personalization system that can model dynamic and contextual profiles to provide more intelligent personalized services. Most of the current systems are not able to adapt to user frequent changing behaviours, and ignore the fact that users might have different preferences in different situations and contexts. Aiming to address these limitations in current personalization systems, this thesis focuses on the aspects of modelling conceptual user profiles that are dynamic and contextual in a content-based platform. The novelty is in the way that these profiles are learnt, adapted, exploited and integrated to infer not just highly relevant items, but also provide such items in the right situation.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:573021 |
| Date | January 2012 |
| Creators | Hawalah, Ahmad |
| Publisher | University of Essex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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