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How recommender systems applied in personal knowledge management environments can improve learning processes
Thaul W, Bleimann U, Wentzel C, Clarke NL
Plymouth e-Learning Conference 2009, Plymouth, UK, 23-24 April, 2009
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Recommender systems combine historical data on user preferences, information filtering and
the application of patterns to suggest and predict items a user might be looking for. Being
successful in a range of e-Business and e-Commerce applications, recommender systems can
also be used in the academic area to support students and researchers at work. This paper
addresses the possible inclusion of recommender systems in personal knowledge management
(PKM) environments by proposing different methods and techniques.

When it comes to personal knowledge, it is hard to get a handle on the information and
knowledge overflow, whether it consists of explicit or tacit one. The usage of current PKM
software systems can support users in dealing with their existing knowledge and information
base, but it only rarely can help them to enlarge it with relevant new aspects. By extending
these tools with the recommender systems methodologies, a new intelligent information and
knowledge access can be offered. Here the user’s existing knowledge base can be a perfect
starting point for new recommendations. Beside the common interpretation of users’
behaviour, as well as by analysing the existing knowledge base with all its keywords,
abstracts and relevant information, recommender systems can gain an additional advantage
from the nature of many academic documents. The system can try to find the referenced
documents, either internally in its own database or externally, and could so offer the user
direct access to it and by doing so directly support the user’s learning process.

Thaul W, Bleimann U, Wentzel C, Clarke NL