Earticle

초록

영어

Many people enjoy digital music (e.g., MP3 songs), usually with random play mode, or their own favorable play list that they have composed. However, such play modes do not consider and support listener preferences of feeling or mood changing with time. Usually listeners have dynamic, not static, demands on music based on their arbitrary situation or mood (e.g., when studying, exercising, being sorrowful, being happy, etc.), so an adaptive algorithm to meet the momentary demand is required. This paper proposes an adaptive algorithm to recommend favorable songs successively, and enable people to seamlessly keep listening to favorable songs, without the action of skipping disliked ones. The algorithm monitors if a listener likes or dislikes a song currently being played. Once the algorithm detects that a listener likes the song, the algorithm recommends the next song that is most similar to the current song. Otherwise, the algorithm recommends quite a different style of a song as the next one, by recognizing that the listener now has a different demand. In our experiment, the proposed algorithm showed better performance, in terms of reducing the action of frequently skipping songs, than random play mode, with statistical significance.

목차

Abstract
 1. Introduction
 2. The Proposed Algorithm
  2.1. Part I: prerequisite processing steps
  2.2. Part II: recommendation steps
 3. Experiment and Result
  3.1. Experiment setup
  3.2. Result analysis
 4. Related Work
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Taek Lee [ Korea University ]
• Hoh Peter In [ Korea University ] Corresponding author.

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605