Exploiting contextual information is considered a good solution to improve the quality of recommendations, aiming at suggesting more relevant items for a specific context. On the other hand, recommender systems research still strive for solving the cold-start problem, namely where not enough information about users and their ratings is available. In this paper we propose a new rating prediction algorithm to face the cold-start...

Recommender systems are software tools and techniques which aim at suggesting to users items they might be interested in. Context-aware recommender systems are a particular category of recommender systems which exploit contextual information to provide more adequate recommendations. However, recommendation engines still suffer from the cold-start problem, namely where not enough information about users and their ratings is available....

Recommendation engines aim to propose users items they are interested in by looking at the user interaction with a system. However, individual interests may be drastically influenced by the context in which decisions are taken. We present an attempt to model user interests via a set of contextual conditional preferences. We show that usage of proposed preferences gives reasonable values of the accuracy and the precision even when...

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...

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...