A Utility Function for the Music Consumer Based on Emotions

Abstract

The objective of this research is to propose a utility function that calculates the possible level of satisfaction that a music consumer would achieve after listening to a song according to the emotional response obtained from a song. Assuming that the consumer's musical preferences are satisfied if the predominant emotions of his current emotional state increase; the probabilistic emotional-musical model (Lugos Abarca, 2023) is used to construct such a utility function. By posing a hypothetical scenario, realistic results were obtained, which guarantee, in principle, its use in theoretical practice. The originality of this research lies in proposing a mathematical tool to study, quantitatively, the decision making of a musical consumer influenced by his emotions. The main conclusion being its importance in the music industry, since the function could predict, theoretically, whether a song will be accepted or rejected by the consumer in comparison with the competition.

A Model for Generating Playlists based on Consumer Emotions

“…Given the premise of the importance of playlists in the music industry and the growing research interest in generating playlists to meet the preferences of music consumers (Bonnin & Jannach, 2014;Choi, Khlif & Epure, 2020;De Mooij & Verhaegh, 1997;Hagen, 2015;Kamehkhosh, Jannach & Bonnin, 2018;Pichl, Zangerle & Specht, 2016;Sanchez, 2018), we seek to contribute to this area with a mathematical model for playlist creation based on consumer emotional utility (Lugos Abarca, 2024). Specifically, the model determines the playback order of songs, progressively increasing emotional utility.…”

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The article proposes a mathematical model for creating playlists based on the emotional utility of the consumer, aiming to maintain high user interest in music streaming platforms (Lugos Abarca, 2024). The proposed method follows these steps: first, it calculates the MEEPE of Songs on the digital platform; subsequently, it quantitatively defines the Consumer's MEEPI before exposure to music; then, it operates the MEEPE of the Songs with the consumer's initial MEEPI using the known resolution process (Lugos Abarca, 2023); finally, it employs an emotional utility function to determine possible levels of emotional satisfaction and establish the playback order.

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