An Agent-Based System with Personalization and Intelligent Assistance Services for Facilitating Knowledge Sharing
The scenario of distributed knowledge in organization, lack of understanding of knowledge sharing benefits and technology inadequacies are the main barriers to knowledge sharing facilitation. A more user-centered application through personalization and intelligent assistance technique are identif...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2006
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/5195/1/FSKTM_2006_5.pdf |
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| Summary: | The scenario of distributed knowledge in organization, lack of understanding of
knowledge sharing benefits and technology inadequacies are the main barriers to
knowledge sharing facilitation. A more user-centered application through
personalization and intelligent assistance technique are identified as the evolution in
knowledge sharing facilitation research.
As response to these challenges, this study is dedicated to approach knowledge
sharing facilitation with an agent-based system. Agent technology is a promising
solution to knowledge sharing facilitation. Agent technology could provide
personalization and intelligent assistance to give a more human-centered approach
towards users in knowledge sharing participation.
This thesis focuses on automatic interest identification and knowledge member
recommendation in order to reduce user’s tasks and ease them to participate in knowledge sharing. The proposed agent based system is called KSFaci (Knowledge
Sharing Facilitator). KSFaci provides personalization and intelligent assistance to
users by offering knowledge member recommendation according to their interest
preferences. This timely action gives users resources to find help and they can
interact with each other to share or exchange knowledge.
The first agent, Profiler is able to monitor user navigational behavior and build user
profile on behalf of the user. The Recommender agent then determines the user’s
most preferred interest and matches them against other users sharing similar interest.
The main algorithms used are profile determination and user similarity. The
recommendation services provided reduce users burden from manual browsing and
searching for knowledge reference resources. KSFaci is embedded in web
environment and is implemented using Java Servlet and runs under Apache server.
The performance of KSFaci is evaluated using a four-factor evaluation metrics
covering the user profile preciseness, recommendation service, staff directory and
document repository. Several techniques have been used including weighted respond
analysis, two-point scale, Likert-scale survey analysis and overlap analysis.
User satisfaction result indicate that the agent-based approach used; by identifying
user’s interests and establishing knowledge network based on interests of its users is
capable in facilitating knowledge sharing. In conclusion, the recommended
knowledge network created based on the automatic interest identification has now
become medium for users to refer for knowledge sources and later perform
knowledge sharing tasks. |
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