1 research outputs found
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, being inspired by such scenarios. A key challenge is that of
presenting the results to the users in an understandable and clear-cut fashion
in order to facilitate the analysis of the results. Indeed, such scenarios
evoke the opportunity to rank and group the results according to specific
criteria. A further challenge consists of presenting the results to the user in
an asynchronous fashion, i.e. the 'push' mode, along with the 'pull' mode, in
which the user explicitly issues a query, and displays the results. Moreover,
an important issue to consider in real-life cases is the possibility of
submitting a query to multiple providers, and collecting the various results.
We have designed and implemented an ontology-based matchmaking system that
suitably addresses the above challenges. We have conducted a comprehensive
experimental study, in order to investigate the usability of the system, the
performance and the effectiveness of the matchmaking strategies with real
ontological datasets.Comment: 28 pages, 8 figure