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Titel
Feature-combination hybrid recommender systems for automated music playlist continuation
AutorInnenVall, Andreu
Herausgeber / HerausgeberinWidmer, Gerhard
Erschienen in
The Journal of Personalization Research, 2018, Jg. 28, H. 4-5
ErschienenSpringer, 2018
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)automated music playlist continuation · hybrid recommender systems · / cold-start problem · music information retrieval · feature extraction
URNurn:nbn:at:at-ubl:3-303 Persistent Identifier (URN)
DOI10.1007/s11257-018-9215-8 
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 Das Werk ist gemäß den "Hinweisen für BenützerInnen" verfügbar
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Feature-combination hybrid recommender systems for automated music playlist continuation [0.64 mb]
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Zusammenfassung (Englisch)

Music recommender systems have become a key technology to support

the interaction of users with the increasingly larger music catalogs of on-line music

streaming services, on-line music shops, and personal devices. An important task in

music recommender systems is the automated continuation of music playlists, that

enables the recommendation of music streams adapting to given (possibly short) listening sessions. Previous works have shown that applying collaborative filtering to

collections of curated music playlists reveals underlying playlist-song co-occurrence

patterns that are useful to predict playlist continuations. However, most music collections exhibit a pronounced long-tailed distribution. The majority of songs occur only

in few playlists and, as a consequence, they are poorly represented by collaborative

filtering. We introduce two feature-combination hybrid recommender systems that

extend collaborative filtering by integrating the collaborative information encoded

in curated music playlists with any type of song feature vector representation. We

conduct off-line experiments to assess the performance of the proposed systems to

recover withheld playlist continuations, and we compare them to competitive pure

and hybrid collaborative filtering baselines. The results of the experiments indicate

that the introduced feature-combination hybrid recommender systems can more accurately predict fitting playlist continuations as a result of their improved representation

of songs occurring in few playlists

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CC-BY-Lizenz (4.0)Creative Commons Namensnennung 4.0 International Lizenz