1. What do listeners attend to when listening to music? : Toward explainable music recommendationsKosar Seyyedhosseinzadeh, Matevž Pesek, Marko Tkalčič, 2026, published scientific conference contribution 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
Published in RUP: 26.06.2026; Views: 420; Downloads: 7
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2. On the role of dwell time for implicitly profiling museum visitorsAlessio Ferrato, Giuseppe Sansonetti, Marko Tkalčič, 2026, published scientific conference contribution Abstract: How long visitors spend viewing artworks, often referred to as dwell time, has long been studied in museology as a potential behavioral indicator of engagement. However, dwell time may encode both genuine preference and situational factors such as fatigue, and disentangling these signals for computational modeling has received limited attention. This study investigates whether dwell time can serve as a valid preference indicator for implicit user modeling and whether incorporating it can improve artwork recommendation. Using the BIRD dataset, which includes eye-tracking data for extracting dwell times and explicit preferences from 51 museum visitors, we report three main findings. First, visitors spend significantly longer (9.27 seconds on average) viewing artworks they like, with a large effect size (Cohen’s d = 1.47). Second, we confirm the museum fatigue phenomenon, the progressive decline in visitor attention throughout a visit, observing a 34% reduction in dwell time from visit start to end. Third, we evaluate collaborative filtering approaches and find that while purely implicit models using dwell time alone perform near-chance level, a hybrid approach that uses dwell time to compute item similarities while predicting preferences from explicit likes achieves the best performance (AUC-ROC = 0.755, AP = 0.522). These findings suggest that dwell time provides complementary information to explicit feedback and can enhance museum recommendation systems when appropriately integrated.
Keywords: implicit, user modeling, recommender systems, artwork, museum
Published in RUP: 09.06.2026; Views: 569; Downloads: 72
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