A simple way to more accurately recommend Music

1. A simple way to more accurately recommend Music Isabella Douzoglou Under the supervision of MitsunoriOgihara & Burt Rosenberg

2. Intro • Problem • Making a system that can calculate which song a user will like based on ratings. • Design • Structure and movement. • Future • Goals • Determining Attributes • Theories about attribute creation. • Application to other problems • Different ways to solve the same problem • Conclusion • The message & feedback.

3. Problem • Gathering Music • Research within the subject of underground electronic music • Something I do myself, but wrote a program to do it for me • Attributes • Are used to describe aspects of the songs • Set the attribute equal to 1 if it applies, 0 if it does not • The logic behind the system discussed further ahead • Recommending • Math! • Tools • mySQL • PHP

4. Everyone is different • Attributes are a refinement of Tags • Tags are very linear, and if humans make them there will be errors. • Attributes are created by the one who designs the database. • Attributes ease malleability of content to individual users. • The success of sharing information depends on the users perspective of the content. • If we can implement a method to understand the user, what you share to them will have more meaning. • i.e. Facebook ads to the music you have “liked” • Pandora • Limited to a like or dislike • Limited to only the music they have rights to • Repetitive, and not as detailed • Spotify (radio) • Limited to a like or dislike • Reference based on other aspects, (label, related artist) although we don’t know their true methods, Spotify users agree that it can be improved.

5. Attribute creation (this project) The accuracy of the structure of the data is based on the attributes. There must be logic in the existence of attributes • They must be true • (easy to say for things like instrumentation) • They must be proportional • (in the case a track consists a lot of attributes) • does it make it a better choice? • boiling down to essentials • Instrumentation is not necessarily mutually exclusive • They can be ambiguous • (in the case of determining non concrete attribute descriptions) • Emotion • Genre • “Sad Melody” vs. “Melancholy” can be mutually inclusive

6. Attribute Example 'ambient' 'indie', 'minimal', 'progressive', 'loopy', 'jazzie', 'electro', 'techno', 'neoclassical', 'psychedellic’, 'house', 'scorelike', 'lounge’ 'reverb', 'delay', 'electric guitar', 'classic piano', 'electronic piano', 'bass guitar', 'synthesizer', 'bass synth', 'arpeggio', 'male vocal', 'female vocal', 'high pitch string', 'low pitch string', 'sax', 'trumpet', 'flute', 'wind hi', 'wind lo', 'plucked', 'bells', 'vinyl sample', 'vocal sample', 'noise’ 'happy melody', 'sad melody', 'dark melody', 'melancholy', 'abstract', 'surreal', 'organic', 'hopeful', 'challenging’ 'weird', 'spacie', 'mysterious', 'spooky/haunting', 'seductive', 'angellic', 'uplifting', 'depressing’ 'agressive', '4.4', 'bouncing', 'standard drumset' Genre Instrumentation Emotion Descriptive Percussive

7. Design (rating) Current user: Isabella First suggestion is given by determining which genre the user prefers. Once a song is displayed, user is expected to listen to it, and give a score from 1-10. Based on this number we determine which attributes it applies to, then sum the result of the multiplication of score and attribute to the total score. 5 (rating given by user) • Steps: • 1. Multiply by attribute • 2. Add to attribute score • in user score vector • 3. Update has Listened • 5 0 5 5 Old New Has Listened:

8. Design (recommending) Has Listened: Current user vector scores: • Steps: • 1. Check if listened. • 2. Multiply user score vector by every attribute of every track in the DB • 3. Every result will become a sum of all its attributes. • 4. Suggest the highest sum • 3. Repeat For display purposes only (this is a loop): Song 1 is highest, but we already heard it.. Recommend Song 4!

9. Attribute creation (other projects) • This method can be applied to any kind of data • The key in its accuracy is your mastery of the subject • Contradictions of individuals when designing the database • Its hard enough to get two people to agree, imagine a group… • Implies the importance of consistency • Being Rational • A strong sense of rationality in designing the database = the results you desire • Alternate designs to data • Attribute weights • At the top of each attribute • Aggregation of scores • Changing the math (+-) or (-1+1)

10. The future & UI • I will be spending next semester in making this available to the public • Feedback and interest in being a test subject will be appreciated • I want to know how well this works for people. • Analogue Scoring • Instead of thinking of a number, thinking of “volume” • More natural and intuitive • In the “near” future • Rewrite the whole program, set up a server with the Database, begin creating the “community” of music • Over the summer/next semester • In the “far” future • Having rights to all of the music in the database to be played • So the user spends time to listen on the website. 10 9 8 7 6 5 4 3 2 1 0

11. Conclusion • Take this design with you • Use it if applicable to your projects • We should put time an effort to projects inspired by human behavior • Focus on questions that computers can't answer • Further exploring yourself, may inspire your projects

12. The end! i.douzoglou@umiami.edu Thank you MitsunoriOgihara& Burt Rosenberg