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Publication . Part of book or chapter of book . Conference object . 2017

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

Andrzej M. J. Skulimowski;
Open Access
Published: 11 Jun 2017
Publisher: Zenodo
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.
Subjects by Vocabulary

Microsoft Academic Graph classification: Process (engineering) Recommender system Field (computer science) Computer science Research groups Data science Incremental learning Cognition


Research recommenders, scientific big data, Personal Learning Environments, preference modelling, mobile and ubiquitous learning

Funded by
Training towards a society of data-savvy information professionals to enable open leadership innovation
  • Funder: European Commission (EC)
  • Project Code: 693092
  • Funding stream: H2020 | RIA
Validated by funder
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Part of book or chapter of book
License: cc-by
Providers: UnpayWall