Cognitive Content Recommendation in Digital Knowledge Repositories – A Survey of Recent Trends

  • Andrzej M. J. SkulimowskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10246)


This paper presents an overview of the cognitive aspects of content recommendation process in large heterogeneous knowledge repositories and their 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 the 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 the 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 primary application field of the above algorithms is a large knowledge repository coupled with an innovative training platform developed within an ongoing Horizon 2020 research project.


Research recommenders Scientific big data Personal learning environments Preference modelling Mobile and ubiquitous learning 



This paper has been supported by the EU Horizon 2020 research project MOVING ( under Contract No. 693092. Selected preliminary results concerning recommendation systems trends have been obtained during the project SCETIST ( financed by the ERDF and contributed to MOVING.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Chair of Automatic Control and Biomedical Engineering, Decision Science LaboratoryAGH University of Science and TechnologyKrakówPoland
  2. 2.International Centre for Decision Sciences and Forecasting, Progress & Business FoundationKrakówPoland

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