Conference paper Open Access
This paper presents an overview of the cognitive aspects of content recommendation process in large heterogeneous knowledge repositories. It also covers applications to design algorithms of incremental learning of users’ preferences, emotions, and satisfaction. This allows the recommendation procedures to align to the present and expected cognitive states of a user, increasing combined recommendation and repository use efficiency. The learning algorithm takes into account the results of the cognitive and neural modelling of users’ decision behaviour. Inspirations from nature used in recommendation systems differ from the usual mimicking of biological neural processes. Specifically, a cognitive knowledge recommender may follow a strategy to discover emotional patterns in user behaviour and then adjust the recommendation procedure accordingly. The knowledge of cognitive decision mechanisms helps to optimize recommendation goals. Other cognitive recommendation procedures assist users in creating consistent learning or research groups. The anticipated primary application field of the above algorithms is a large knowledge repository coupled with an innovative training platform developed within the ongoing Horizon 2020 research project MOVING.