An Implicit-Semantic Tag Recommendation Mechanism for Socio-Semantic Learning Systems
In recent years Social Tagging (ST) has become a popular functionality in social learning environments, not least because tags support the exchange of users’ knowledge representations, a process called social sensemaking. An important design feature of ST-Systems (STS) is the tag recommendation service. Several principles for tag recommendation mechanisms (TRM) have been proposed, which are built upon a technical and statistical perspective on STS and based on aggregated user data on a word level. Up to now, a cognitive perspective also taking into account memory processes has been neglected. In this paper we therefore introduce a TRM that applies a formal theory of human memory to model a user’s prototypical tag configurations. The algorithm underlying the TRM is supposed to recommend psychologically plausible tag combinations and to mediate social sensemaking.
KeywordsTagging Categorization Cognitive Modelling MINERVA2 Tag-Recommendation-Algorithm
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