Chia-Min LinによるMusiac 音楽推薦サービスは、インターフェース・インタラクション・UXデザインコンペティションカテゴリーにおける非常に権威ある2020 A' Design Award 銀賞の優れた受賞作品です。

銀インターフェース· 2020

Musiac 音楽推薦サービス

デザイナー： Chia-Min Lin

クライアント: LinStudio

インターフェースカテゴリーのA' Design Award 銀賞受賞者

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A' Design Awardで見るプレスリリース受賞バッジ

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デザイン

Musiac 音楽推薦サービスについて

Musiacは音楽レコメンデーションエンジンであり、積極的な参加を利用して、ユーザー向けの正確なオプションを見つけます。アルゴリズムの独裁に挑戦するための代替インターフェースを提案することを目的としています。情報フィルタリングは、避けられない検索アプローチになっています。ただし、エコーチャンバー効果を作成し、ユーザーの好みに厳密に従うことにより、ユーザーを快適ゾーンに拘束します。ユーザーは受動的になり、マシンが提供するオプションの質問をしなくなります。オプションを確認するために時間を費やすと、莫大なバイオコストが増加する可能性がありますが、有意義な体験を生み出すのはその努力です。

受賞

A' Design Award 銀賞について

Musiac 音楽推薦サービスは、見事で印象的なハイエンドのデザインに授与される、非常に権威ある評価である A' Design Award 銀賞の受賞作品です。銀賞の受賞作品は、きわめて輝かしく創造性に富んだ作品であり、デザイナーの傑出した専門知識、才能、洞察力を示しています。Musiac 音楽推薦サービスは、素晴らしい代表作であり、驚くほどプロフェッショナルで、深く革新的であることが期待されます。この A' Design Award 銀賞を受賞したデザインは、非常に注目すべき称賛される発明であり、芸術・科学・デザイン・技術の境界に寄与し、それを前進させます。Musiac 音楽推薦サービスは、多くの人に羨まれる創造物であり、欲求を呼び覚まし、前向きな感情をもたらし、驚きと感嘆を灯します。

デザインの裏側

プレスキット

デザイナーが提供するクレジット、チームメンバー、技術的な詳細。

独自の特性: 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.
寸法とパッケージ: 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.
タグ: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
製造技術: 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.
デザインの課題: 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.
リサーチ: 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.
インスピレーション: 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.
プロジェクト期間: Musiac was a one year project done in 2018, Detroit.
オペレーションフロー: 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.
画像クレジット: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.