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Title:What do listeners attend to when listening to music? : Toward explainable music recommendations
Authors:ID Seyyedhosseinzadeh, Kosar (Author)
ID Pesek, Matevž (Author)
ID Tkalčič, Marko (Author)
Files:.pdf RAZ_Seyyedhosseinzadeh_Kosar_2026.pdf (1,04 MB)
MD5: 3F2AC38FA970D3A1211694C8C45FADC6
 
.pdf RAZ_Seyyedhosseinzadeh_Kosar_2026.pdf (1,04 MB)
MD5: 3F2AC38FA970D3A1211694C8C45FADC6
 
Language:English
Work type:Unknown
Typology:1.08 - Published Scientific Conference Contribution
Organization:FAMNIT - Faculty of Mathematics, Science and Information Technologies
Abstract:Personalized explanations in recommender systems can be useful when they reflect the aspects of an item that matter to the user. To account for individual preferences regarding different aspects of songs in music recommender systems, it is first necessary to identify which song aspects explain meaningful variation among listeners. This paper introduces a questionnaire instrument designed to operationalize these differences as a measurable user characteristic. The instrument was developed through item generation, expert review, pilot testing, and a main study. Data from the main study (� = 245) were analyzed using exploratory factor analysis to examine the questionnaire’s internal structure. The results supported a two-factor solution, interpreted as Lyric- Engagement and Music-Engagement, with both dimensions showing good internal consistency. These findings suggest that listeners’ orientations toward lyrical and musical elements can be measured in an interpretable way. The contribution of this study lies not in proposing a new explanation algorithm, but in providing an empirical basis for user modeling that may help align explanation content with the song characteristics most relevant to different listeners.
Keywords:music recommender systems, explainable recommendation, personalized explanations, user modeling, questionnaire development, exploratory factor analysis
Publication version:Version of Record
Year of publishing:2026
Number of pages:Str. 10-18
PID:20.500.12556/RUP-23183 This link opens in a new window
UDC:004.8
ISSN on article:1613-0073
COBISS.SI-ID:282804483 This link opens in a new window
Publication date in RUP:26.06.2026
Views:34
Downloads:1
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Record is a part of a proceedings

Title:UMAP-WS 2026
COBISS.SI-ID:280125443 This link opens in a new window

Record is a part of a journal

Title:CEUR workshop proceedings
Shortened title:CEUR workshop proc.
Publisher:M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen.
ISSN:1613-0073
COBISS.SI-ID:12740630 This link opens in a new window

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:N2-0354-2024
Name:Določanje uporabniške izkušnje z računalniškim psihološkim modeliranjem

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Abstract:Personalizirane razlage v priporočilnih sistemih so lahko koristne, kadar odražajo tiste vidike izdelka oziroma vsebine, ki so za uporabnika pomembni. Da bi v glasbenih priporočilnih sistemih upoštevali individualne preference glede različnih vidikov pesmi, je treba najprej ugotoviti, kateri vidiki pesmi pojasnjujejo pomembne razlike med poslušalci. Ta članek predstavlja vprašalnik, zasnovan za operacionalizacijo teh razlik kot merljive uporabniške značilnosti. Vprašalnik je bil razvit skozi več faz: oblikovanje postavk, pregled strokovnjakov, pilotno testiranje in glavno raziskavo. Podatki iz glavne raziskave (N = 245) so bili analizirani z eksploratorno faktorsko analizo, da bi preučili notranjo strukturo vprašalnika. Rezultati so podprli rešitev z dvema faktorjema, ki sta bila interpretirana kot vključenost v besedilo (Lyric-Engagement) in vključenost v glasbo (Music-Engagement). Obe dimenziji sta izkazali dobro notranjo zanesljivost. Ugotovitve kažejo, da je mogoče usmerjenost poslušalcev k besedilnim in glasbenim elementom pesmi meriti na razumljiv in interpretabilen način. Prispevek te raziskave ni v predlaganju novega algoritma za razlago priporočil, temveč v zagotavljanju empirične osnove za modeliranje uporabnikov, ki lahko pomaga uskladiti vsebino razlag s tistimi značilnostmi pesmi, ki so za različne poslušalce najbolj pomembne.
Keywords:glasbeni priporočilni sistemi, razložljiva priporočila, faktorska analiza sistemov, personalizirane razlage, uporabniško modeliranje, razvoj vprašalnika


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