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<p>In this article, we have introduced Gamispotify. For the first time, in a social network-based environment, and by benefiting from gamification and crowdsourcing, Gamispotify recommends music to the users based on their personal values. The proposed method has been compared to another published article by conducting two experiments and surveys on 32 application participants. Experimental results indicate that user engagement has significantly increased using the proposed method. Spending time on the application increased by 299.48%, the music play count increased by 150%, and receiving music recommendations increased by 244.41%. Statistical analysis using the Wilcoxon Signed-Rank Test confirmed these improvements were significant (p &lt; 0.05). Moreover, survey results support these findings and show a notable increase in user satisfaction with the proposed music recommendation system compared with the previous work (p = 0.019). It also indicates that the proposed method successfully motivated users to discover new songs (p = 0.043). We have also found that social networking features, recommendations based on personal values, and gamification have positively impacted the users’ motivation to use the music streaming application.</p>

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Gamispotify: a gamified social music recommendation system based on users’ personal values

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月1日

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  • Mohammad Hajarian,
  • Miguel Herrera Carrillo,
  • Paloma Díaz,
  • Ignacio Aedo

摘要

In this article, we have introduced Gamispotify. For the first time, in a social network-based environment, and by benefiting from gamification and crowdsourcing, Gamispotify recommends music to the users based on their personal values. The proposed method has been compared to another published article by conducting two experiments and surveys on 32 application participants. Experimental results indicate that user engagement has significantly increased using the proposed method. Spending time on the application increased by 299.48%, the music play count increased by 150%, and receiving music recommendations increased by 244.41%. Statistical analysis using the Wilcoxon Signed-Rank Test confirmed these improvements were significant (p < 0.05). Moreover, survey results support these findings and show a notable increase in user satisfaction with the proposed music recommendation system compared with the previous work (p = 0.019). It also indicates that the proposed method successfully motivated users to discover new songs (p = 0.043). We have also found that social networking features, recommendations based on personal values, and gamification have positively impacted the users’ motivation to use the music streaming application.