Uwe Kirschenmann

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Recommender systems are widely used online to support users in finding relevant information. They can be based on different techniques such as content-based and collaborative filtering. In this paper, we introduce a new way of similarity calculation for item-based collaborative filtering. Thereby we focus on the usage of an object and not on the object's(More)
— The growing amount of available information on the internet makes the process of filtering appropriate information an increasing challenge. Because currently existing approaches provide insufficient results in many cases, we propose a new way of relating objects based on their usage. We assume that objects which are significantly often used in the same(More)
In this paper, we introduce a new way of detecting semantic similarities between learning objects by analyzing their usage in a web portal. Our approach does not rely on the content of the learning objects or on the relations between the users and the learning objects but on usage-based relations between the objects themselves. The technique we apply for(More)
—Positive emotions have been proven to be a key factor for successful learning. In modern personalized learning environments informal learning takes a prominent role and with this the use of computer-mediated communication. Communication-data, like for example chat-logs, can be harvested for sentiments. Most sentiment-analyses operate processing verbal(More)
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