Recommender systems aim to propose potentially interesting items to a user based on his preferences or previous interaction with the system. In the last decade, researcher found out that known recommendation techniques are not sufficient to predict user decisions. It has been noticed that user preferences strongly depend on the context in which he currently is. This raises new challenges for the researchers such as how to obtain...

Recommender systems are software tools and techniques which aim at suggesting new items that may be of interest to a user. This dissertation is focused on four problems in recommender systems domain. The first one is context-awareness, i.e. how to obtain relevant contextual information, how to model user preferences in a context and use them to make predictions. The second one is multi-domain recommendation, which aim at suggesting...

In this chapter we present a new approach to development of modularized knowledge bases. We argue that modularization should start from the very beginning of modeling, i.e. from the conceptualization stage. To make this feasible, we propose to exploit a context-oriented, semantic approach to modularization. This approach is based on the Structural Interpretation Model (SIM) presented earlier elsewhere. In the first part of thischapter...

In the paper we present the results of experiment we performed to determine whether a contextual approach may be used to increase the performance of querying a knowledge base. For the experiments we have used a unique setting where we put much effort in developing a contextual and a non-contextual ontology which are as much close counterparts as possible. To achieve this we created a contextual version...

Recommender Systems are software tools and techniques which aim at suggesting new items that may possibly be of interest to a user. Context-Aware Recommender Systems exploit contextual information to provide more adequate recommendations. In this paper we described a modification of an existing contextual post-filtering algorithm which uses rules-like user representation called Contextual Conditional Preferences. We extended the...