Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/50762
DC FieldValueLanguage
dc.contributor.authorBellini, Vitoit
dc.contributor.authorDi Sciascio, Eugenioit
dc.contributor.authorDonini, Francesco Mariait
dc.contributor.authorPomo, Claudioit
dc.contributor.authorRagone, Azzurrait
dc.contributor.authorSchiavone, Angeloit
dc.date.accessioned2024-02-12T17:07:11Z-
dc.date.available2024-02-12T17:07:11Z-
dc.date.issued2024it
dc.identifier.issn1573-7675it
dc.identifier.urihttp://hdl.handle.net/2067/50762-
dc.description.abstractKnowledge Graphs (KGs) have already proven their strength as a source of high-quality information for different tasks such as data integration, search, text summarization, and personalization. Another prominent research field that has been benefiting from the adoption of KGs is that of Recommender Systems (RSs). Feeding a RS with data coming from a KG improves recommendation accuracy, diversity, and novelty, and paves the way to the creation of interpretable models that can be used for explanations. This possibility of combining a KG with a RS raises the question whether such an addition can be performed in a plug-and-play fashion – also with respect to the recommendation domain – or whether each combination needs a careful evaluation. To investigate such a question, we consider all possible combinations of (i) three recommendation tasks (books, music, movies); (ii) three recommendation models fed with data from a KG (and in particular, a semantics-aware deep learning model, that we discuss in detail), compared with three baseline models without KG addition; (iii) two main encyclopedic KGs freely available on the Web: DBpedia and Wikidata. Supported by an extensive experimental evaluation, we show the final results in terms of accuracy and diversity of the various combinations, highlighting that the injection of knowledge does not always pay off. Moreover, we show how the choice of the KG, and the form of data in it, affect the results, depending on the recommendation domain and the learning model.it
dc.format.mediumELETTRONICOit
dc.language.isoengit
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleA qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencodersit
dc.typearticle*
dc.identifier.doi10.1007/s10844-023-00830-zit
dc.identifier.scopus2-s2.0-85182686731it
dc.identifier.isiWOS:001145062400001it
dc.identifier.urlhttps://link.springer.com/article/10.1007/s10844-023-00830-zit
dc.relation.journalJOURNAL OF INTELLIGENT INFORMATION SYSTEMSit
dc.relation.numberofpages21it
dc.subject.scientificsectorING-INF/05it
dc.subject.keywordsData integrationit
dc.subject.keywordsDeep learningit
dc.subject.keywordsKnowledge graphit
dc.subject.keywordsSemanticsit
dc.subject.keywordsRecommendationit
dc.subject.ercsectorPE6it
dc.description.numberofauthors6it
dc.description.internationalit
dc.contributor.countryITAit
dc.contributor.countryDEUit
dc.type.refereeREF_1it
dc.type.miur262*
item.fulltextWith Fulltext-
item.openairetypearticle-
item.cerifentitytypePublications-
item.grantfulltextrestricted-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.journal.journalissn1573-7675-
crisitem.journal.anceE204539-
Appears in Collections:A1. Articolo in rivista
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