Recommender Systems and Technologies in Artificial Intelligence

Dear Colleagues,

Today, thanks to an increasingly wider range of context-aware sensing opportunities, the user, can be provided with modulated sensorial support in their performance. This increases the scope of building recommender systems aimed at providing emotion-aware recommendations within different ambient intelligence frameworks. 

In particular, there is strong evidence from previous research that emotions have an important effect on a user's performance in different tasks and contexts, where their mental state and subsequent behaviour present challenges for the successful deployment of automated or semi-automated systems. This covers a wide range of development areas, from self-driving cars to recommender systems in education and to intelligence guidance and coaching health professionals. 

We propose a Special Issue that addresses the problem of detecting, modelling and reacting to those mental states of each user, which affect their readiness and disposition to cooperate with the system in achieving higher success and performance rates. 

This Special Issue is particularly interested in papers that either provide evidence that detecting each user's emotions, behaviour, context and reactions makes a difference or show progress in building accurate, user-centred recommender systems that take advantage of an increasing number of data for each user in different scenarios and tasks, bearing in mind that the real problem is to model the user within them. 

This Special Issue aims to cover the recent emerging trends and applications for user-centred recommender systems. Topics of interest include but are not limited to:

• Wearable-based affect-recognition systems;
• Affect-recognition systems from low-cost, off-the-shelf devices;
• Sensor data quality assessment and management;
• Multimodal affect-recognition systems;
• Unobtrusive affect-recognition systems;
• Machine-learning/artificial-intelligence approaches to obtaining personalized models for affect recognition;
• Intelligent recommender systems in user-centred scenarios (e.g., medicine, education, transportation, etc.);
• Context-aware recommendation systems;
• Methods and tools for affect-aware intelligent tutoring systems;
• Behavioural interventions;
• Adaptive systems that improve the performance of the user;
• Affect-aware user models in semi-automated systems;
• Experimental datasets;
• Regulations and standards;
• Privacy and ethics regarding user-centred recommender systems.

Prof. Dr. Jesús G. Boticario
Prof. Dr. David Martín Gómez
Dr. Ana Serrano Mamolar
Guest Editors

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• recommender systems
• affect-aware recommender
• physiological sensors
• video processing and recognition
• user-centred modelling
• data mining
• context information processing
• personalization and ambient intelligence