Using Appropriate Context Models for CARS Context Modelling

Abstract

Most context-aware recommender systems in the literature that use context modelling have the tendency to develop domain and application specific context models that limit, even eliminate any reuse and sharing capabilities. Developers and researchers in the field struggle to design their own context models without having a good understanding of context and without using any reference models for guidance, often resulting in overspecialized, inefficient or incomplete context models. In this work we build upon prior work to propose an enhanced online context modelling system for Context-Aware Recommender Systems. The system supports CARS developers in the process of building their own context models from scratch, while it supports at the same time sharing and reuse of the models among developers. The system was tested with a real dataset with positive results, as it was able to support context model development with instructions to the developer, model comparison, useful statistics, recommendations of similar models, as well as alternative views of context models to aid the developer’s task.