Usability Fujinaga 2003

Design criteria for music recommender systems 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)

Use existing research into factors affecting musical taste 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

Uitdenbogerd, A. , and R. Schyndel. 2002 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

Factors affecting music preference 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

Genres / styles Moods AllMusicGuide.com: 531 Amazon,com: 719 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

Techniques for music recommenders Collaborative filtering 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

Information needs (music as information) 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

Purpose: To relate descriptions of affect to specific musical works 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

Two main problems in MIR research 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

First Principles of MIR: 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.

Evaluation of four web-accessible music libraries. 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

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