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

Abstract

Abstract. 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.

Visions of a Future Research Workplace Arising from Recent Foresight Exercises

“…This allows the agents to pursue the search autonomously and simultaneously, until a prescribed stack level or the desired retrieval goal is achieved. A creativity-stimulating content-based search and recommendation has been investigated within the recent Horizon 2020 project (Skulimowski 2017a). The design of GES knowledge provision procedures must ensure that the reply to each query is given at a specified level of trust.…”

“…GESs will be capable of processing "big data" to "big knowledge". New knowledge fusion methods will be developed, such as hybrid and scenario-based anticipatory networks (Skulimowski 2014a), e-science foresight (Skulimowski 2016b), including combinations of forecasts (Elliott and Timmermann 2004) or recommendations (Skulimowski 2017a). Finally, Sect.…”

Optimal selection from a set of offers using a short list

“…However, there may exist a conflict of interest between the goals of the writer or implementer of the algorithm (a seller or intermediary) and the DM (e.g. Skulimowski, 2017). In this case, it would be natural to examine game theoretic models of such search procedures.…”