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Factors Affecting Music Recommender Success: A Review

This study reviews the factors influencing the success of music recommenders, including design criteria, user preferences, collaborative filtering, content-based analysis, and limitations. It also explores existing research on musical taste and factors like age, occupation, gender, and familiarity with music.

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Factors Affecting Music Recommender Success: A Review

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  1. Usability Ichiro Fujinaga McGill University

  2. Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender success. International Symposium on Music Information Retrieval. 204-8. • Design criteria for music recommender systems • Survey of research into musical taste • Review of music recommenders • Provide personalized content to users • Messages • List of stories • Artwork • Collaborative filtering (collect users’ opinions, ranking) • Content-based filtering • Limitations: • Inadequate raw data (editorial information) • Lack of quality control (user preference) • Lack of user preferences for new recordings • Content-based analysis needed for new recordings • Presentation (mostly simple lists)

  3. Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender success. International Symposium on Music Information Retrieval. 204-8. • Goals • Simple to use with minimum of input • More effort in providing input lead to better recommendations • Choice of music based on preferences, style, or mood • Use existing research into factors affecting musical taste • Social psychology • Demographics for marketing

  4. Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender success. International Symposium on Music Information Retrieval. 204-8. • Existing research • Stable extraverts: solid predictable music • Stable introverts: classical and baroque styles • Unstable extraverts: romantic music expressing overt emotions • Unstable introverts: mystical and impressionistic romantic works • Aggressive: heavy metal or hard rock • Japanese adolescents: classical or jazz • Critical age: mean 23.5 years old • Occupation • Dressmakers: moderately slow • Typist: fast tempo • Socio-economic background • Upper class women: classical • Working class men: hillbilly (Indiana) • Consistency in ranking of classical and popular music • Enjoyment correlates to labeling (“romantic”, “Nazi”, none) or known composer’s name

  5. Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender success. International Symposium on Music Information Retrieval. 204-8. • Factors affecting music preference • Age • Origin • Occupation • Socio-economic background • Personality • Gender • Musical education • Familiarity with the music or style • Complexity of music • Lyrics

  6. Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender success. International Symposium on Music Information Retrieval. 204-8. • Genres / styles • AllMusicGuide.com: 531 • Amazon.com: 719 • MP3.com 430 • Moods • 8 clusters with 67 moods (Hevner) • 10 clusters with 52 moods (Farnsworth 1958) • Features: tempo, tonality, distinctiveness of rhythm, pitch height

  7. Uitdenbogerd, A., and R. Schyndel. 2002. A review of factors affecting music recommender success. International Symposium on Music Information Retrieval. 204-8. Techniques for music recommenders • Collaborative filtering • Feedback from users: ratings, annotations, time spent • Content-based filtering • Problem of extracting musical semantics from raw signal • Low-level features; notes, timbre, rhythm • High-level features: adjectives • Transcription, instrument identification, genre classifier • Similarity measure from user supplied example (Welsh et al.) • 1248 features, 10-15 second samples, k-NN

  8. Kim, J.-Y., and N. Belkin. 2002. Categories of music description and search terms and phrases used by non-music experts. International Symposium on Music Information Retrieval. 209-14. • Information needs (music as information) • Information-seeking towards the satisfaction of user • Why does the user seek information? • What purpose does the user believe it will serve? • What use does it serve when found? • Three basic “human needs” • Physiological (food, water, shelter) • Affective (emotional needs, e.g.: attainment, domination) • Cognitive (need to plan, need to learn skills) • Music IR has concentrated on cognitive needs • Not enough user need studies • Ignored affective needs • Ignored musical information needs

  9. Kim, J.-Y., and N. Belkin. 2002. Categories of music description and search terms and phrases used by non-music experts. International Symposium on Music Information Retrieval. 209-14. • Purpose: To relate descriptions of affect to specific musical works • “means” for listeners to express their information “needs” • Seven classical music: 22 subjects • 11 s.: Words to describe the music • 11 s.: Words used to search for the music • Words used grouped into seven categories • Mostly emotions and occasions or filmed events • Subjects had no formal musical training • Used non-formal music terms • Terms not found in music query systems

  10. Futrelle, J., and J. Stephen Downie. 2002. Interdisciplinary communities and research issues in music information retrieval. International Symposium on Music Information Retrieval. 215-21. • Two main problems in MIR research • No evaluation method • Lack of user-need studies • Overemphasis on research in QBH systems is unsupportable given their doubtful usefulness • Research into recommender systems common in other domain is inexplicably rare • Lack of user interface research • Undue emphasis on Western music

  11. Futrelle, J., and J. Stephen Downie. 2002. Interdisciplinary communities and research issues in music information retrieval. International Symposium on Music Information Retrieval. 215-21. First Principles of MIR: • MIR systems are developed to serve the needs of particular user communities. • MIR techniques are evaluated according to how well they meet the needs of user communities. • MIR techniques are evaluated according to agreed-upon measures against agreed-upon collections of data, so that meaningful comparisons can be made between different research efforts.

  12. Blandford, A., and H. Stelmaszewska. 2002. Usability of musical digital libraries: A multimodal analysis. International Symposium on Music Information Retrieval. 231-7. Evaluation of four web-accessible music libraries. • www.nzdl.org music • www.nzdl.org video • ABC Tunefinder • Folk Music Collection • Aimed at different user community (different levels of technological and musical knowledge) • Too many file format choice for novices

  13. Lee, J., J. Downie, and S. Cunningham. 2005. Challenges in cross-cultural/multilingual music information seeking. Proceedings of the International Conference on Music Information Retrieval. 1-7.

  14. Leong, T., F. Vetere, and S. Howard. 2006. Randomness as a resource for design. In Proceedings of the 6th ACM Conference on Designing interactive Systems, 132-9. • Randomwebsearch.com • randomwebsite.com/ • www.strangebanana.com/generator.aspx

  15. Other usability studies • Variations (Indiana Music Library) • Design guidelines and user-centered digital libraries (Theng et al.)

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