Abstract
We introduce a new recommending paradigm based on the genomic features of the candidate objects. The system is based on the tree structure of the object metadata which we convert in acceptance rules, leaving the user the discretion of selecting the most convincing rules for her/his scope. We framed the deriving recommendation system on a content management platform within the scope of the European Project NETT and tested it on the Entree UCI benchmark.
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This work has been supported by the European Project NETT.
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Apolloni, B., Bassis, S., Mesiti, M., Valtolina, S., Epifania, F. (2016). A Rule Based Recommender System. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_9
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DOI: https://doi.org/10.1007/978-3-319-33747-0_9
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