Earticle

초록

영어

Current context-aware adaptation techniques are limited in their support for user
personalisation. Complex codebases, a reliance on developer modification and an inability to
automatically learn from user interactions hinder their use for tailoring behaviour to
individuals. To address these problems we have devised a personalised, dynamic, run-time
approach to adaptation. The approach provides techniques for selecting the relevant
information from a user’s behaviour history, for mining usage patterns, and for generating,
prioritising, and selecting adaptation behaviour. Our evaluation study shows that the
proposed mining approach is more accurate than rule-based and neural network methods
when compared to actual user choices.

목차

Abstract
 1. Introduction
 2. A mining approach to adaptation
 2.1. Overview
 2.2. User modelling
 2.3. Relevant case selection
 2.4. Mining behaviour patterns
 2.5. Adaptation rule selection
 3. Evaluation study and results
  3.1. Study set-up and implementations
  3.2. Findings
  3.3. Case study validity
 4. Related work
 5. Conclusions
 6. References

저자

• Shiu Lun Tsang [ Distributed Systems Group Trinity College Dublin ]
• Siobhán Clarke [ Distributed Systems Group Trinity College Dublin ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605