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Social Navigation through Recommendations

Knowledge Exchange Lab


October 2008 - open-end


Leibniz Graduate School for Knowledge Media Research


Social navigation as a guidance instrument: Community members evaluate available objects (e.g. documents, discussion contributions) actively via explicit ratings, or passively based on their usage data. Consequently, recommendations for prospective users can be derived improving the navigation and retrieval of relevant data especially in situations of informational overload. Implementing this approach in the informal learning context attempts to demonstrate the mentioned benefits.

Taking advantage of the communities' intelligence is the central idea pursued in this project. A personalised learning path can be suggested to each individual by the application of social navigation. Depending on the rating dimensions (e.g. relevance, novelty or incongruity) and community affiliations new perspectives are pointed out to activate critical thinking and reflection. In particular we are referring to new types of recommendations which are not based on the principle of maximum similarity.

The project is focused on cognitive processes caused by social navigation. Accordingly the general effectiveness of recommender systems in the context of learning will be explored.