An Ontology-based Tool for Dynamic Generation, Classification and Recommendation of Novel Contents in Online Libraries

In this work we present AMARETTO (dynAMic generAtoR of novEl conTenT in bOoks), an intelligent recommender system exploiting a nonmonotonic extension of Description Logics with typical properties and probabilities to dynamically generate novel contents in Goodreads, the largest website for readers and book recommendations (https://www.goodreads.com). The tool AMARETTO can be used to both the generation/suggestion of novel genres of books and the reclassification of the available items within such new genres. AMARETTO first extracts a prototypical description of the available genres by means of a standard information extraction pipeline, then it generates novel classes of genres as the result of an ontology-based combination of such extracted representations, by exploiting the reasoning capabilities of a probabilistic extension of a Description Logic of typicality. We have tested AMARETTO by reclassifying the available books in Goodreads with respect to the new generated genres, as well as with an evaluation, in the form of a controlled user study experiment, of the feasibility of using the obtained reclassifications as recommended contents. The obtained results are encouraging and pave the way to many possible further improvements and research directions.