Music for the Seasons: Seasonal Music Preferences in College Students

Pettijohn, Terry F.; Williams, Greg; Carter, Tiffany C.

doi:10.1007/s12144-010-9092-8

Cited by 30 publications

(21 citation statements)

References 22 publications

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“…e most frequently considered types of context include location (e.g., listening at workplace, when commuting, or relaxing at home) [100] and time (typically categorized into, e.g., morning, a ernoon, and evening) [32]. Context may, in addition, also relate to the listener's activity [184], weather [140], or the use of di erent listening devices, e.g., earplugs on a smartphone vs. hi-stereo at home [76], to name a few. Since music listening is also a highly social activity, investigating the social context of the listeners is crucial to understand their listening preferences and behavior [46,134].…”

Section: Particularities Of Music Recommendationmentioning

confidence: 99%

Current challenges and visions in music recommender systems research

Schedl

Zamani

Chen

et al. 2018

Int J Multimed Info Retr

260

155

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Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's ngertip. While today's MRS considerably help users to nd interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user-item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. e purpose of this trends and survey article is twofold. We rst identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the eld. e article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the eld.

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Section: Particularities Of Music Recommendationmentioning

confidence: 99%

Current challenges and visions in music recommender systems research

Schedl

Zamani

Chen

et al. 2018

Int J Multimed Info Retr

260

155

View full text Add to dashboard Cite

show abstract

“…The first set of studies concerns the most covert but already known environmental attributes unattached to sensual perceptions, like season, time of day, and even geographical situation. For instance, musical selection may reflect seasonal influences, though refused by Yu and Kang (Yu & Kang, 2010); "To every song, there is a season … reflexive and complex music when primed with fall/winter and energetic and rhythmic and upbeat and conventional music when primed with spring/summer" (Pettijohn, Williams, & Carter, 2010). As having a war zone experience as an environmental condition may increase the length of the melodies (Cerulo, 1984), geographical regions and being near to centers of musical activity influence on originality of composer's work (Simonton, 1986).…”

Section: Personal Attributes-basic Layermentioning

confidence: 99%

Graphical Layout of the Musical Preferences Studies: An Overview on How the Studies on Musical Tastes Are Conducted

Tayyebi¹,

Demir²,

Nemutlu³

et al. 2020

ADR

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Various studies have been exploring the numerous determinants form our musical tastes and some review papers classify them in a discursive manner to provide a more holistic understanding of the determinants. This study, as a similar endeavor, depicts the mains determinants of our musical preferences categorized with regards to our musical perception process via a graphical model. After dividing the internal and external factors, the model provides a layer-based structure illustrating both musical taste determinants and modes of investigation. This paper provides three layers of determinants. The basic layer reflects the trace of more fundamental aspects of music on our musical tastes, like tempo as well as aggressive or happy as perceived attributes. The classification layer discusses on the trace of some classes in musical appreciations, like genre and Big Five Model. As lastly, the skimmed layer, as the midst layer, illustrates the newly-developed factors summing up the determinants of our musical taste. The introduced three layers also classify the external determinants of our musical taste. Consequently, this study not only provides a more holistic understanding of the determinants but also reflects the gap among the conducted studies to provide a platform for further investigations.

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“…For instance, people usually prefer different types of music in different seasons [27]. Consequently, music recommendation approaches with environment-related parameters perform better than those without considering contextual information.…”

Section: Environment-related Context Based Approachesmentioning

confidence: 99%

Learning to embed music and metadata for context-aware music recommendation

et al. 2017

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Contextual factors greatly influence users' musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users' contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users' historical listening records. Specifically, the proposed approach first learns music pieces' embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users' global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user's preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.

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Music for the Seasons: Seasonal Music Preferences in College Students

Cited by 30 publications

References 22 publications

Current challenges and visions in music recommender systems research

Current challenges and visions in music recommender systems research

Graphical Layout of the Musical Preferences Studies: An Overview on How the Studies on Musical Tastes Are Conducted

Learning to embed music and metadata for context-aware music recommendation

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