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dc.contributor.authorGatzioura, Anna
dc.contributor.authorVinagre, João
dc.contributor.authorJorge, Alípio Mário
dc.contributor.authorSànchez-Marrè, Miquel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2020-05-26T07:59:07Z
dc.date.available2020-05-26T07:59:07Z
dc.date.issued2021-05-01
dc.identifier.citationGatzioura, A. [et al.]. A hybrid recommender system for improving automatic playlist continuation. "IEEE transactions on knowledge and data engineering", 1 Maig 2021, vol. 33, núm. 5, p. 1819-1830.
dc.identifier.issn1041-4347
dc.identifier.urihttp://hdl.handle.net/2117/188974
dc.description.abstractAlthough widely used, the majority of current music recommender systems still focus on recommendations’ accuracy, userpreferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations’ quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address “similar concepts” rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items’ discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs’ similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.
dc.description.sponsorshipThis work has been partially supported by the Catalan Agency for Management of University and Research Grants (AGAUR) (2017 SGR 574), by the European Regional Development Fund (ERDF), through the Incentive System to Research and Technological development, within the Portugal2020 Competitiveness and Internationalization Operational Program –COMPETE 2020– (POCI-01-0145-FEDER006961), and by the Portuguese Foundation for Science and Technology (FCT) (UID/EEA/50014/2013).
dc.format.extent12 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshRecommender systems (Information filtering)
dc.subject.lcshComputer algorithms
dc.subject.otherHybrid recommender system
dc.subject.otherAutomatic playlist continuation
dc.subject.otherMusic recommender systems
dc.subject.otherLatent dirichlet allocation
dc.subject.otherCase-based reasoning
dc.subject.otherBeyond accuracy dimensions
dc.titleA hybrid recommender system for improving automatic playlist continuation
dc.typeArticle
dc.subject.lemacSistemes recomanadors (Filtratge d'informació)
dc.subject.lemacAlgorismes computacionals
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.1109/TKDE.2019.2952099
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8894369
dc.rights.accessOpen Access
local.identifier.drac28505126
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/RIS3CAT/2017 SGR 574
local.citation.authorGatzioura, A.; Vinagre, J.; Jorge, A.; Sànchez-Marrè, M.
local.citation.publicationNameIEEE transactions on knowledge and data engineering
local.citation.volume33
local.citation.number5
local.citation.startingPage1819
local.citation.endingPage1830


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