디자이너 Chia-Min Lin의 작품 Musiac 음악 추천 서비스은(는) 매우 권위 있는 2020 A' Design Award 은상의 인터페이스, 인터랙션 및 사용자 경험 디자인 부문에서 탁월한 수상작입니다.

은인터페이스· 2020

Musiac 음악 추천 서비스

디자이너: Chia-Min Lin

클라이언트: LinStudio

인터페이스 부문 A' Design Award 은상 수상작

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디자인

Musiac 음악 추천 서비스 소개

Musiac은 음악 추천 엔진으로 사전 참여를 활용하여 사용자를위한 정확한 옵션을 찾습니다. 알고리즘 독재에 도전하는 대안 인터페이스를 제안하는 것을 목표로합니다. 정보 필터링은 필연적 인 검색 접근 방식이되었습니다. 그러나 에코 챔버 효과를 생성하고 사용자의 선호도를 교묘하게 따르기 때문에 사용자가 자신의 안락한 영역에있는 것을 제한합니다. 사용자는 수동적이되어 기계가 제공하는 옵션에 대해 의문을 제기하지 않습니다. 옵션을 검토하는 데 시간을 투자하면 엄청난 바이오 비용이 증가 할 수 있지만 의미있는 경험을 만드는 것은 노력입니다.

수상 인정

A' Design Award 은상 소개

Musiac 음악 추천 서비스, 이 작품은 A' Design Award 은상 수상작으로, 최상급이며 아름답고 인상적인 디자인에 수여되는 비범하게 권위 있는 인정입니다. 은상 수상작은 대단히 뛰어나고 매우 창의적인 작품으로, 디자이너의 탁월한 전문 지식, 재능, 통찰력을 보여 줍니다. Musiac 음악 추천 서비스, 이 작품은 훌륭한 대표작으로서 매우 전문적이고 깊이 있게 혁신적일 것으로 기대됩니다. 이 은상 수상 디자인은 예술, 과학, 디자인, 기술의 경계를 넓히는 데 이바지하는 주목할 만하고 찬사를 받는 발명품입니다. Musiac 음악 추천 서비스, 이 작품은 모두가 부러워하는 창작물로서 욕망을 일깨우고 긍정적인 감정을 불러오며 경탄과 경이로움을 불러일으킵니다.

디자인 비하인드

프레스 킷

디자이너가 제공한 크레딧, 팀원 및 기술 세부 사항.

고유 특성: 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.
치수 및 패키징: 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.
태그: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
생산 기술: 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.
디자인 과제: 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.
연구: 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.
영감: 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.
프로젝트 기간: Musiac was a one year project done in 2018, Detroit.
운영 흐름: 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.
이미지 출처: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.