Musiac 音乐推荐服务 by Chia-Min Lin is a highly esteemed winner of the very prestigious 2020 A' Design Award 银奖 in 界面、交互与用户体验设计 Competition Category.

银界面· 2020

Musiac 音乐推荐服务

by Chia-Min Lin

Client: LinStudio

A' Design Award 银奖 Winner in 界面 Category

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Verified Award Winner
Verify Award Status for #91014

View at A' Design Award Press Release Award Badge

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The Design

About Musiac 音乐推荐服务

Musiac是音乐推荐引擎，利用主动参与为其用户找到精确的选择。它旨在提出替代接口来挑战算法专制性。信息过滤已成为必然的搜索方法。但是，它会产生回音室效果，并严格按照用户的喜好将其限制在其舒适区域。用户变得被动，不再质疑机器提供的选项。花时间审查选项可能会增加巨大的生物成本，但这是创造有意义的体验的努力。

The Recognition

About the A' Design Award 银奖

Musiac 音乐推荐服务是 A' Design Award 银奖得主，这是授予顶级、华美而令人印象深刻的设计的极为崇高的认可。银奖得主是极其卓越、极具创造力的作品，展现出其设计师杰出的专业技能、才华与敏锐。预计Musiac 音乐推荐服务将成为一件出色的典范之作，兼具非凡的专业性与深刻的创新性。这件赢得 A' Design Award 银奖的设计是一项备受瞩目、令人钦佩的发明，造福并拓展艺术、科学、设计和技术的边界。Musiac 音乐推荐服务是一件令人羡慕的创作，它唤起渴望，带来积极的情感，并激发惊叹与赞叹。

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.