How recommender systems applied in personal knowledge management environments can improve learning processes
Witold Skrzypczyk, Udo Bleimann, Christoph Wentzel, Nathan Clarke · 2009
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 studen ts and researchers at work. This paper addresses the possible inclusion of recommender sys tems 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 i t 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 ex isting 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 kno wledge base with all its keywords,