1. Usability
Fujinaga 2003

2. Design criteria for music recommender systems
Uitdenbogerd, A., and R. Schyndel A review of factors affecting music recommender success. International Symposium on Music Information Retrieval
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. Use existing research into factors affecting musical taste
Uitdenbogerd, A., and R. Schyndel A review of factors affecting music recommender success. International Symposium on Music Information Retrieval
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
Uitdenbogerd, A., and R. Schyndel A review of factors affecting music recommender success. International Symposium on Music Information Retrieval
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. Factors affecting music preference
Uitdenbogerd, A., and R. Schyndel A review of factors affecting music recommender success. International Symposium on Music Information Retrieval
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. Genres / styles Moods AllMusicGuide.com: 531 Amazon,com: 719
Uitdenbogerd, A., and R. Schyndel A review of factors affecting music recommender success. International Symposium on Music Information Retrieval
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. Techniques for music recommenders Collaborative filtering
Uitdenbogerd, A., and R. Schyndel A review of factors affecting music recommender success. International Symposium on Music Information Retrieval
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, second samples, k-NN

8. Information needs (music as information)
Kim, J.-Y., and N. Belkin Categories of music description and search terms and phrases used by non-music experts. International Symposium on Music Information Retrieval
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. Purpose: To relate descriptions of affect to specific musical works
Kim, J.-Y., and N. Belkin Categories of music description and search terms and phrases used by non-music experts. International Symposium on Music Information Retrieval
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. Two main problems in MIR research
Futrelle, J., and J. Stephen Downie Interdisciplinary communities and research issues in music information retrieval. International Symposium on Music Information Retrieval
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. First Principles of MIR:
Futrelle, J., and J. Stephen Downie Interdisciplinary communities and research issues in music information retrieval. International Symposium on Music Information Retrieval
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. Evaluation of four web-accessible music libraries.
Blandford, A., and H. Stelmaszewska Usability of musical digital libraries: A multimodal analysis. International Symposium on Music Information Retrieval
Evaluation of four web-accessible music libraries.
music
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. Other usability studies
Variations (Indiana Music Library)
Design guidelines and user-centered digital libraries (Theng et al.)