http://dl.gi.de/handle/20.500.12116/5015 Wed, 22 Jul 2026 22:00:43 GMT 2026-07-22T22:00:43Z http://dl.gi.de/handle/20.500.12116/5157 Zum maschinellen Lernen für benutzeradaptive Systeme am Beispiel Bayes'scher Netze Wittig, Frank Der vorliegende Beitrag beleuchtet die engen Zusammenhänge zwischen der Aufgabenstellung beim maschinellen Lernen und der Aufgabenstellung benutzeradaptiver Systeme. Es werden kritische Problemstellungen identifiziert und diskutiert, die typischerweise die direkte Anwendung maschineller Standardlerntechniken im Kontext benutzeradaptiver Systeme erschweren. Im zweiten Teil des Beitrags wird eine Konzeption vorgestellt, die einige dieser Probleme eines Einsatzes maschineller Lernverfahren Bayes’scher Netze in benutzeradaptiven Systemen durch Integration existierender und speziell für die Benutzermodellierung modifizierter bzw. neu entwickelter Verfahren löst. Tue, 01 Jan 2002 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/5157 2002-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/5155 An Ontology-Based Recommendation System for Elderly and Disabled Persons Zinnikus, Ingo; Bogdanovich, Anton; Schäfer, Ralph In this paper, we describe the use of a hierarchical ontology representation as a basis for modelling user preferences in the SAID project for elderly and disabled persons. The nodes of the ontology tree are associated with either pre-selected or dynamically found web sites relevant for each topic of the user s interest (in this way web-surfing can be substituted by browsing through the ontology). In order to further reduce the complexity of the tree, we combine this approach with applying a probability based recommendation system which monitors the user s actions and adapts itself according to the observed behaviour. We also show that the resulting ontology tree is compatible with Bayesian networks and discuss limitations and possible extensions. Tue, 01 Jan 2002 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/5155 2002-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/5156 Reasoning from Context Zimmermann, Andreas; Lorenz, Andreas; Specht, Marcus The paper describes a new approach of using the Case-Based Reasoning (CBR) methodology in context-aware systems. We elaborate how this technique can be applied to generate recommendations based on the contexts of users respectively objects especially in a mobile scenario. By combining CBR methodology and context awareness a new and powerful way of modelling and reasoning from contexts emerges. Using CBR cases to enclose the context will then enhance the possibilities to compare contexts, determine certain values of context-similarities, and reflect this information in the process of generating recommendations. Tue, 01 Jan 2002 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/5156 2002-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/5154 Automated System for Personal Learning and Control of Students Knowledge Joukova, Irina; Siplivaya, Marina This paper describes original technology for problems solving and basic principles of design of automated learning and knowledge control system. This system is created as open adaptive hyper media system with personalized access to educational repositories, navigation, learning strategy and control. Such system is a necessary fool for users, because it provides special educational tools for distance learning. It includes: definitions of learning aims; definitions of beginning user’s knowledge; choice of learning strategy; dynamic course materials and personal tests generation; projects development; consultations with teacher; common projects discussion; projects evaluation by teacher. System will make it possible to implement open mobile personalized learning with access to information resources. Tue, 01 Jan 2002 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/5154 2002-01-01T00:00:00Z