Inferring preferences in ontology-based recommender systems using WOWA

In content-based semantic recommender systems the items to be considered are defined in terms of a set of semantic attributes, which may take as values the concepts of a domain ontology. The aim of these systems is to suggest to the user the items that fit better with his/her preferences, stored in the user profile. When large ontologies are considered it is unrealistic to expect to have complete information about the user preference on each concept. In this work, we explain how the Weighted Ordered Weighted Averaging operator may be used to deduce the user preferences on all concepts, given the structure of the ontology and some partial preferential information. The parameters of the WOWA operator enable to establish the desired aggregation policy, which ranges from a full conjunction to a full disjunction. Different aggregation policies have been analyzed in a case study involving the recommendation of touristic activities in the city of Tarragona. Several profiles have been compared and the results indicate that different aggregation policies should be used depending on the type of user. The amount of information available in the ontology must be also taken into account in order to establish the parameters of the proposed algorithm.