Chia-Min Lin ᱨᱮᱱᱟᱜ Musiac ᱥᱮᱨᱮᱧ ᱵᱟᱛᱟᱣ ᱥᱮᱵᱟ

Chia-Min Lin ᱨᱮᱱᱟᱜ Musiac ᱥᱮᱨᱮᱧ ᱵᱟᱛᱟᱣ ᱥᱮᱵᱟ ᱫᱚ ᱤᱱᱴᱟᱨᱯᱷᱮᱥ, ᱤᱱᱴᱟᱨᱮᱠᱥᱚᱱ ᱟᱨ ᱵᱮᱵᱦᱟᱨᱤᱭᱟᱹ ᱟᱱᱩᱵᱷᱟᱵ ᱰᱤᱡᱟᱭᱤᱱ ᱯᱨᱚᱛᱤᱡᱚᱜᱤᱛᱟ ᱵᱷᱟᱜ ᱨᱮ 2020 A' Design Award ᱨᱩᱯᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱨᱮᱱᱟᱜ ᱟᱹᱰᱤ ᱢᱟᱱ ᱟᱠᱟᱱ ᱡᱤᱛᱠᱟᱨ ᱠᱟᱱᱟ᱾

ᱨᱩᱯᱟᱤᱱᱴᱟᱨᱯᱷᱮᱥ· 2020

Musiac ᱥᱮᱨᱮᱧ ᱵᱟᱛᱟᱣ ᱥᱮᱵᱟ

Chia-Min Lin ᱞᱮᱠᱟᱛᱮ

ᱜᱟᱦᱟᱠ: LinStudio

ᱤᱱᱴᱟᱨᱯᱷᱮᱥ ᱵᱷᱟᱜ ᱨᱮ A' Design Award ᱨᱩᱯᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱡᱤᱛᱠᱟᱨ

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ᱡᱟᱸᱪ ᱟᱠᱟᱱ ᱮᱣᱟᱨᱰ ᱡᱤᱛᱠᱟᱨ

#91014 ᱞᱟᱜᱤᱫ ᱮᱣᱟᱨᱰ ᱦᱟᱞᱚᱛ ᱡᱟᱸᱪ

A' Design Award ᱨᱮ ᱧᱮᱞ ᱢᱮ ᱯᱨᱮᱥ ᱨᱤᱞᱤᱡ ᱯᱩᱨᱥᱠᱟᱨ ᱵᱮᱡ

ᱨᱚᱝ

ᱰᱤᱡᱟᱭᱤᱱ

Musiac ᱥᱮᱨᱮᱧ ᱵᱟᱛᱟᱣ ᱥᱮᱵᱟ ᱵᱟᱵᱚᱛ ᱛᱮ

ᱢᱭᱩᱡᱤᱭᱟᱠ ᱫᱚ ᱢᱤᱫ ᱥᱮᱨᱮᱧ ᱵᱟᱛᱟᱣ ᱤᱧᱡᱤᱱ ᱠᱟᱱᱟ, ᱡᱟᱦᱟᱸ ᱞᱟᱦᱟᱛᱮ ᱥᱮᱞᱮᱫ ᱵᱮᱵᱷᱟᱨ ᱠᱟᱛᱮ ᱵᱮᱵᱷᱟᱨᱤᱭᱟᱹ ᱠᱚ ᱞᱟᱹᱜᱤᱫ ᱴᱷᱤᱠ ᱠᱩᱥᱤ ᱠᱚ ᱯᱟᱱᱛᱮᱭᱟᱭ᱾ ᱱᱚᱣᱟ ᱫᱚ ᱟᱞᱜᱚᱨᱤᱫᱚᱢ ᱮᱠᱞᱟ ᱥᱟᱥᱚᱱ ᱪᱟᱞᱟᱣ ᱠᱷᱚᱱ ᱪᱮᱞᱮᱧᱡᱽ ᱞᱟᱹᱜᱤᱫ ᱵᱷᱮᱯᱮᱫ ᱤᱱᱴᱚᱨᱯᱷᱮᱥ ᱠᱚ ᱵᱟᱛᱟᱣ ᱞᱟᱹᱜᱤᱫ ᱢᱚᱱᱮ ᱛᱟᱭᱟ᱾ ᱠᱷᱚᱵᱚᱨ ᱪᱷᱟᱹᱱᱤ ᱫᱚ ᱢᱤᱫ ᱵᱟᱝ ᱵᱟᱹᱜᱤᱜ ᱠᱟᱱ ᱥᱮᱸᱫᱽᱨᱟ ᱦᱚᱨ ᱵᱚᱱ ᱦᱩᱭ ᱟᱠᱟᱱᱟ᱾ ᱢᱮᱱᱠᱷᱟᱱ, ᱱᱚᱣᱟ ᱫᱚ ᱤᱠᱳ ᱪᱮᱢᱵᱚᱨ ᱚᱨᱥᱚᱝ ᱛᱮᱭᱟᱨᱟ ᱟᱨ ᱵᱮᱵᱷᱟᱨᱤᱭᱟᱹ ᱠᱚ ᱟᱠᱚᱣᱟᱜ ᱯᱚᱥᱚᱱᱫᱽ ᱛᱮ ᱠᱚᱸᱡᱤ ᱠᱟᱛᱮ ᱟᱠᱚᱣᱟᱜ ᱥᱩᱠ ᱡᱟᱜᱟ ᱨᱮ ᱠᱚ ᱫᱚᱦᱚ ᱠᱟᱜ-ᱟ᱾ ᱵᱮᱵᱷᱟᱨᱤᱭᱟᱹ ᱠᱚ ᱯᱮᱥᱤᱵᱷ ᱦᱩᱭᱩᱜ-ᱟ ᱟᱨ ᱢᱮᱥᱤᱱ ᱡᱟᱦᱟᱸ ᱠᱩᱥᱤ ᱮᱢᱟ ᱠᱚᱣᱟ ᱚᱱᱟ ᱠᱚ ᱫᱩᱲᱩᱵ ᱵᱚᱸᱫᱽ ᱠᱟᱜ-ᱟ᱾ ᱠᱩᱥᱤ ᱠᱚ ᱧᱮᱞ ᱪᱤᱠᱤ ᱞᱟᱹᱜᱤᱫ ᱚᱠᱛᱚ ᱞᱟᱜᱟᱣ ᱫᱚ ᱡᱤᱭᱟᱹᱞᱤ ᱠᱷᱚᱨᱚᱪ ᱵᱟᱲᱦᱟᱣ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ, ᱢᱮᱱᱠᱷᱟᱱ ᱱᱚᱣᱟ ᱫᱚ ᱚᱱᱟ ᱠᱩᱨᱩᱢᱩᱴᱩ ᱠᱟᱱᱟ ᱡᱟᱦᱟᱸ ᱢᱤᱫ ᱢᱟᱱᱮᱯᱷᱩᱞ ᱡᱤᱭᱚᱱ ᱛᱮᱭᱟᱨᱟ᱾

ᱢᱟᱱᱟᱱ

A' Design Award ᱨᱩᱯᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱵᱟᱵᱚᱛ ᱛᱮ

Musiac ᱥᱮᱨᱮᱧ ᱵᱟᱛᱟᱣ ᱥᱮᱵᱟ ᱫᱚ A' Design Award ᱨᱩᱯᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱧᱟᱢ ᱟᱠᱟᱱᱟ, ᱱᱚᱶᱟ ᱫᱚ ᱥᱩᱱᱫᱚᱨ ᱟᱨ ᱵᱷᱟᱞᱟᱭ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ ᱞᱟᱹᱜᱤᱫ ᱮᱢ ᱟᱠᱟᱱ ᱥᱚᱢᱟᱱ ᱠᱟᱱᱟ᱾ ᱱᱚᱶᱟ ᱠᱚ ᱫᱚ ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱠᱚ ᱨᱮᱭᱟᱜ ᱵᱷᱟᱵᱱᱟ ᱟᱨ ᱵᱷᱟᱞᱟᱭ ᱠᱟᱢᱤ ᱡᱷᱟᱞᱠᱟᱣ ᱠᱚ᱾ Musiac ᱥᱮᱨᱮᱧ ᱵᱟᱛᱟᱣ ᱥᱮᱵᱟ ᱫᱚ ᱠᱚᱞᱟ, ᱣᱤᱜᱭᱟᱱ, ᱰᱤᱡᱟᱭᱤᱱ ᱟᱨ ᱴᱮᱠᱱᱚᱞᱚᱡᱤ ᱨᱮᱭᱟᱜ ᱥᱤᱢᱟ ᱵᱟᱲᱦᱟᱣ ᱟᱨ ᱫᱩᱱᱤᱭᱟ ᱵᱷᱟᱞᱟᱭ ᱞᱟᱹᱜᱤᱫ ᱜᱚᱲᱚ ᱮᱢ ᱠᱟᱱᱟ᱾

ᱰᱤᱡᱟᱭᱤᱱ ᱯᱤᱪᱷᱟ ᱨᱮ

ᱯᱨᱮᱥ ᱠᱤᱴ

ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱧᱮᱞ ᱠᱷᱚᱱ ᱠᱨᱮᱰᱤᱴ, ᱴᱤᱢ ᱥᱫᱚᱥᱭ ᱟᱨ ᱴᱮᱠᱱᱤᱠᱮᱞ ᱵᱤᱵᱚᱨᱚᱬ ᱮᱢ ᱟᱠᱟᱱᱟ᱾

ᱟᱞᱜᱟ ᱜᱩᱱ ᱠᱚ: Musiac is a music recommendation service aims to provide better selection mechanism. It uses a contour map to represent all musical genres in a single interface, showing the relationship across songs and genres in a 2D space. It maps out a taste bubble from the user's playlists, separating the user's prior musical taste field from the unexplored musical territory. The user can change the outline of the bubble by dragging it and expanding the scope of the recommendation algorithm.
ᱢᱟᱯ ᱟᱨ ᱯᱮᱠᱮᱡᱤᱝ: Musiac is a desktop application. It requires a user to import his/her playlists of third-party apps to build the "Taste Bubble" at the beginning. The bubble sits on an algorithmically-generated genres map, with a horizontal (electric, acoustic) and a vertical (atmospheric, bouncier) indication. The bubble separates the familiar and unexplored territory for a user so he/she could expand the recommendation (temporarily), without changing their behavior.
ᱴᱮᱜ ᱠᱚ: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
ᱛᱮᱭᱟᱨ ᱛᱮᱠᱱᱚᱞᱚᱡᱤ: Musiac learns from "Every Noise at Once", has a virtual database, a theoretical possible algorithmically-generated map, and an ethical interface to reduce the bio-cost of finding liked songs from enormous options. It starts with a study of recommendation engines and filters, interviews, and a pre-testing, then ideates with "what if" questions along the generative design process, run through 2 round of user-testing to validate initial concepts and iterates. The project took a year to finalize.
ᱰᱤᱡᱟᱭᱱ ᱪᱮᱞᱮᱧ: The most difficult part of this project is to find an industry professional to talk to me. Online streaming service is a very competitive field. Hence, business and advertising dominate the final result of music recommendations, which creates intransparent and unethical algorithms. Academics and scholars provide enthusiastic feedback, but one of the business executives cancel his help after he saw my questions list.
Another major research constrains is the fuzzy relationships of million users' playlists. Single users' taste bubble might not accommodate with the overarching dimensions, even though the machine could easily map his or her playlists into an individual lump. After I consulted with the famous founder of EveryNoise, he gives me great advice that I should have customized dimensions for different users to solve this issue. The system could map out the contour map from each individual users perspective, based on what's the location of the taste bubble, then decide how to show the rest of the genres. Despite the computation power is not efficient enough for this project, but this solution is definitely feasible at the nearby future.
ᱜᱚᱵᱮᱥᱚᱱᱟ: Musiac ran with a waterfall and agile hybrid methodology: research, framing, prototype, test, and reflecting. Feedback was collected along the way and therefore, multiple iterations. I did solid case studies, interviewed active and passive music online streaming users, and consulted with programmers to finalize the proper frame of this project. Multiple user testings with a clickable prototype were performed in-person, as well as online.
ᱯᱨᱮᱨᱚᱱᱟ: Musiac started with a simple notion. Interface is the tangent point between our perception and reality. I believe interface reflects a subtle world of why we make choices. I ran into a website called Every Noise at Once and found it's a perfect database to test my theory. Interface has a higher-level goal of helping us to process information better. I want to design something that goes beyond data visualization and empower users to be more proactive upon the world they're seeing.
ᱯᱨᱚᱡᱮᱠᱴ ᱥᱚᱢᱚᱭ: Musiac was a one year project done in 2018, Detroit.
ᱠᱟᱹᱢᱤ ᱦᱚᱨ: Musiac is a desktop application with an unusual interface, allows users to control the scope of recommendations and to manipulate the filtered results. It uses a bubble to separate the unexplored genres from the familiar genres. A user could easily change the shape of the bubble to expand the recommendation source. It also provides multiple filters, such as themes with different adjectives; numbers of views, finding songs with specific traits, and a guiding system to go beyond the comfort zone.
ᱪᱤᱛᱟᱹᱨ ᱥᱟᱹᱦᱟ: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.