Towards Ambient Recommender Systems: Results of New Cross-disciplinary Trends

We first introduce ambient recommender systems, which arose from the analysis of new trends in human factors in the next generation of recommender systems. We then explain some results of these new trends in real-world applications. This paper extends current approaches to recommender systems towards a cross-disciplinary perspective by combining the specific advantages from several research areas to achieve better user modelling. Our approach synergistically combines a model of the user's emotional information with intelligent agents and machine learning to: i) provide highly relevant recommendations in everyday life, thus reducing the user's information overload and making human-machine interaction richer and more flexible; and ii) learn about, predict and adapt to the user's behaviour in the next generation of recommender systems in ambient intelligence scenarios. We also describe the results of an application of these new trends to human factors using a cross-domain hybrid approach: i) an e-commerce recommender system about training courses with more than three million users and ii) the prototype of an ambient recommender system for emergency interventions, combining virtual rich-context interaction environments and wearable computing, to train firefighters.