Musiac O Servizo De Recomendacións Musicais de Chia-Min Lin

O deseño Musiac O Servizo De Recomendacións Musicais de Chia-Min Lin é un estimado gañador do moi prestixioso 2020 A' Design Award de Prata na Categoría de Competición Deseño de Interface, Interacción e Experiencia de Usuario.

PrataInterface· 2020

Musiac O Servizo De Recomendacións Musicais

de Chia-Min Lin

Cliente: LinStudio

Gañador do A' Design Award de Prata na Categoría Interface

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Cores

O Deseño

Sobre Musiac O Servizo De Recomendacións Musicais

Musiac é un motor de recomendacións musicais, utiliza unha participación proactiva para atopar opcións precisas para os seus usuarios. Pretende propoñer interfaces alternativas para desafiar a autocracia do algoritmo. O filtrado de información converteuse nun inevitable enfoque de busca. Non obstante, crea efectos de cámara eco e limita aos usuarios na súa zona de confort seguindo con dureza as súas preferencias. Os usuarios fanse pasivos e deixan de cuestionar as opcións que a máquina ofrece. Gastar tempo para revisar as opcións pode aumentar un enorme custo biolóxico, pero é o esforzo o que crea unha experiencia significativa.

O Recoñecemento

Sobre o A' Design Award de Prata

Musiac O Servizo De Recomendacións Musicais gañou o A' Design Award de Prata, un recoñecemento enormemente prestixioso outorgado a deseños de gama alta, fermosos e impresionantes. Os gañadores Prata son obras tremendamente brillantes e sumamente creativas que ilustran a pericia, o talento e a agudeza sobresaíntes dos seus deseñadores. Espérase que Musiac O Servizo De Recomendacións Musicais sexa unha obra marabillosa, escaparate de oficio, notablemente profesional e profundamente innovadora. Este deseño gañador do A' Design Award de Prata é unha invención moi notable e admirada que beneficia e fai avanzar os límites da arte, a ciencia, o deseño e a tecnoloxía. Musiac O Servizo De Recomendacións Musicais é unha creación moi envexada que esperta desexos, achega sentimentos positivos e prende asombro e marabilla.

Detrás do deseño

Kit de prensa

Créditos, membros do equipo e detalles técnicos proporcionados polo deseñador.

Propiedades Únicas: 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.
Dimensións e Embalaxe: 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.
Etiquetas: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Tecnoloxía de Produción: 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.
Reto de Deseño: 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.
Investigación: 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.
Inspiración: 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.
Duración do Proxecto: Musiac was a one year project done in 2018, Detroit.
Fluxo de Operacións: 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.
Créditos das Imaxes: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.