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What do listeners attend to when listening to music? : Toward explainable music recommendationsKosar Seyyedhosseinzadeh,
Matevž Pesek,
Marko Tkalčič, 2026, objavljeni znanstveni prispevek na konferenci
Opis: 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.
Ključne besede: music recommender systems, explainable recommendation, personalized explanations, user modeling, questionnaire development, exploratory factor analysis
Objavljeno v RUP: 26.06.2026; Ogledov: 238; Prenosov: 4
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