Ontological user profiling in recommender systems

Implicit User Profiling in News Recommender Systems

User profiling is an important part of content-based and hybrid recommender systems. These profiles model users’ interests and preferences and are used to assess an item’s relevance to a particular user. In the news domain it is difficult to extract explicit signals from the users about their interests, and user profiling depends on in-depth analyses of users’ reading habits. This is a challeng...

Ontological Matchmaking in Recommender Systems

The electronic marketplace offers great potential for the recommendation of supplies. In the so called recommender systems, it is crucial to apply matchmaking strategies that faithfully satisfy the predicates specified in the demand, and take into account as much as possible the user preferences. We focus on real-life ontology-driven matchmaking scenarios and identify a number of challenges, be...

Survey of User Profiling in News Recommender Systems

Collaborative Evolution for User Profiling in Recommender Systems

Accurate user profiling is important for an online recommender system to provide proper personalized recommendations to its users. In many realworld scenarios, the user’s interests towards the items may change over time. Therefore, a dynamic and evolutionary user profile is needed. In this work, we come up with a novel evolutionary view of user’s profile by proposing a Collaborative Evolution (...

User Friendly Recommender Systems

Recommender systems are a recent but increasingly widely used resource. Yet most, if not all of them suffer from serious deficiencies. Recommender systems often require first time users to enter ratings for a large number of items — a tedious process that often deters users. Thus, this thesis investigated whether useful recommendations could be made without requiring users to explicitly rate it...