Musiac Sèvis Rekòmandasyon Mizik by Chia-Min Lin

Musiac Sèvis Rekòmandasyon Mizik by Chia-Min Lin is a highly esteemed winner of the very prestigious 2020 A' Design Award Ajan in Entèfas Competition Category.

AjanEntèfas· 2020

Musiac Sèvis Rekòmandasyon Mizik

by Chia-Min Lin

Client: LinStudio

A' Design Award Ajan Winner in Entèfas Category

55,925

Verified Award Winner

Verify Award Status for #91014

View at A' Design Award Press Release Award Badge

Colors

The Design

About Musiac Sèvis Rekòmandasyon Mizik

Musiac se yon motè rekòmandasyon mizik, itilize patisipasyon aktif pou jwenn opsyon egzak pou itilizatè li yo. Li vize pou pwopoze entèfas altènatif pou defye otokrasi algorithm lan. Filtrasyon enfòmasyon te vin yon apwòch rechèch inevitab. Sepandan, li kreye efè chanm eko ak kontrenn itilizatè yo nan zòn konfò yo pa strickly swiv preferans yo. Itilizatè yo vin pasif epi yo sispann kesyone opsyon machin lan bay. Pase tan yo revize opsyon ka ogmante yon gwo biyo-pri, men li se efò a ki kreye yon eksperyans siyifikatif.

The Recognition

About the A' Design Award Ajan

Musiac Sèvis Rekòmandasyon Mizik se yon ganyan A' Design Award Ajan, yon rekonesans ki gen yon prestij enmans yo bay design nivo siperyè, bèl anpil e enpresyonan. Ganyan Ajan yo se travay ki trè briyan e ki trè kreyatif ki montre ekspètiz, talan ak finès designer yo. Yo atann Musiac Sèvis Rekòmandasyon Mizik pou se yon bèl travay modèl, remakab nan pwofesyonalis e ki gen gwo inovasyon. Design sa a ki ganyen A' Design Award Ajan se yon envansyon trè remakab, admire ki pote benefis e ki pouse limit atizay, syans, design ak teknoloji. Musiac Sèvis Rekòmandasyon Mizik se yon kreyasyon anpil moun jalou ki reveye dezi, ki pote santiman pozitif e ki limen sipriz ak mèvèy.

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties: 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.
Dimensions & Packaging: 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.
Tags: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Production Technology: 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.
Design Challenge: 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.
Research: 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.
Inspiration: 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.
Project Duration: Musiac was a one year project done in 2018, Detroit.
Operation Flow: 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.
Image Credits: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.