The main purpose of this research is to find an effective way to personalise information
searching on the Internet using middleware search agents, namely, Personalised Search
Agents (PSA). The PSA acts between users and search engines, and applies new and existing
techniques to mine and exploit relevant and personalised information for users.
Much research has already been done in developing personalising filters, as a middleware
technique which can act between user and search engines to deliver more personalised results.
These personalising filters, apply one or more of the popular techniques for search result
personalisation, such as the category concept, learning from user actions and using metasearch
engines. By developing the PSA, these techniques have been investigated and
incorporated to create an effective middleware agent for web search personalisation.
In this thesis, a conceptual model for the Personalised Search Agent is developed,
implemented by developing a prototype and benchmarked the prototype against existing web
search practices. System development methodology which has flexible and iterative
procedures that switch between conceptual design and prototype development was adopted as
the research methodology.
In the conceptual model of the PSA, a multi-layer client server architecture is used by
applying generalisation-specialisation features. The client and the server are structurally the
same, but differ in the level of generalisation and interface. The client handles personalising
information regarding one user whereas the server effectively combines the personalising
information of all the clients (i.e. its users) to generate a global profile. Both client and server
apply the category concept where user selected URLs are mapped against categories. The
PSA learns the user relevant URLs both by requesting explicit feedback and by implicitly
capturing user actions (for instance the active time spent by the user on a URL). The PSA also
employs a keyword-generating algorithm, and tries different combinations of words in a user
search string by effectively combining them with the relevant category values.
The core functionalities of the conceptual model for the PSA, were implemented in a
prototype, used to test the ideas in the real word. The result was benchmarked with the results
from existing search engines to determine the efficiency of the PSA over conventional
searching. A comparison of the test results revealed that the PSA is more effective and
efficient in finding relevant and personalised results for individual users and possesses a
unique user sense rather than the general user sense of traditional search engines.
The PSA, is a novel architecture and contributes to the domain of knowledge web information
searching, by delivering new ideas such as active time based user relevancy calculations,
automatic generation of sensible search keyword combinations and the implementation of a
multi-layer agent architecture. Moreover, the PSA has high potential for future extensions as
well. Because it captures highly personalised data, data mining techniques which employ
case-based reasoning make the PSA a more responsive, more accurate and more effective tool
for personalised information searching.
| Identifer | oai:union.ndltd.org:ADTP/219037 |
| Date | January 2005 |
| Creators | Gopinathan-Leela, Ligon, n/a |
| Publisher | University of Canberra. Information Sciences & Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | ), Copyright Ligon Gopinathan-Leela |
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