We propose a novel hybrid recommendation model in which user preferences and item features are described in terms of semantic concepts defined in domain ontologies. The exploitation of meta-information describing the recommended items and user profiles in a general, portable way, along with the capability of inferring knowledge from the relations defined in the ontologies, are the key aspects of the presented proposal. Taking advantage of the enhanced semantics representation, user profiles are compared at a finer grain size than they are in usual recommender systems. More specifically, the concept, item, and user spaces are clustered in a coordinated way, and the resulting clusters are used to find similarities among individuals at multiple semantic layers. Such layers correspond to implicit Communities of Interest (CoI), and enable collaborative recommendations of enhanced precision. Our approach is tested in two sets of experiments: one including profiles manually defined by real users and another with automatically generated profiles based on data from the IMDb and MovieLens datasets.
In REF the authors propose a hybrid recommendation system, where user preferences and item features are described by semantic concepts to obtain users' clusters corresponding to implicit Communities of Interest.
A Multilayer Ontology-based Hybrid Recommendation Model
HICSS
Computer Science