Mining User Models for Effective Adaptation of Context-Aware Applications

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.

• Type: Conference
• Year: 2007
• Where: IPC
• Authors: Shiu Lun Tsang, Siobhán Clarke
• Keywords: Adaptation Behaviour, Applied Computing, Context-aware Adaptation Techniques, IPC 2007, User’s Behaviour History