Situation awareness from time, position, and student activity to make context-aware recommendations
Classical user context as a base of context awareness is structured as a triple
1. Time
2. Position (home, at school, ... )
3. Background activity
Adding simple student data acquisition we could build and use student context models in context-aware recommendations.
1. Time
2. Position (home, at school, ... )
3. Background activity
Adding simple student data acquisition we could build and use student context models in context-aware recommendations.
Example:
A simple student data (position, background activity ) would be added to MathE portal and this would allow the upgrade recommendations to be contextualized.
Reference:
Mai Abusair et al, Context-aware Recommender System Based on Content Filtering, International Conference on Application of Information and Communication Technologies (AICT ), 2021.
Author of the tip:
Andrej Košir
University of Ljubljana