Musiac Purahéi Ñe'ẽporã Mba'apoha Chia-Min Lin omba'apóva

Musiac Purahéi Ñe'ẽporã Mba'apoha Chia-Min Lin omba'apóva ha'e rembiporuarã oñemba'eguaséva 2020 A' Design Award Pláta pegua Jehechaukaha Jejapo Ñeha'ã Categoría-pe.

PlátaJehechaukaha· 2020

Musiac Purahéi Ñe'ẽporã Mba'apoha

Chia-Min Lin -gua

Mba'eporuhára: LinStudio

A' Design Award Pláta Rembiporuarã Jehechaukaha Categoría-pe

57,305

Premio Rembiporuarã Oñemoneĩva

Emoneĩ Premio Reko #91014 Peg̃ua

Ehecha A' Design Award-pe Marandu Myasãi Mba'ete Jeguerohory

Sa'y

Diseño

Musiac Purahéi Ñe'ẽporã Mba'apoha Rehegua

Musiac ha'e peteĩ purahéi ñe'ẽporã rembiporu, oiporu oñemba'apo tenondegua ojuhu hag̃ua poravorã hekopete oipuruvévape g̃uarã. Ohechakuaa jehechaha ambuéva oñembohovái hag̃ua algoritmo ijeheguiete. Marandu ñembohetague oiko jeheka rape tekotevẽva. Katu, omoheñói jehechaha pupu mba'apo ha oñembojopy'apy oipuruva tenda py'aguapýpe opytávo hekópe iporavopyrévape. Oipuruva oiko py'ỹi ha ndoporanduvéima mba'e ome'ẽva mba'e'oka. Oiporu aravo ohechajey hag̃ua poravorã ikatu ombotuichu mba'e repykue, katu pe mba'apo omoheñói peteĩ tembiapo iporãitéva.

Jeguerohory

A' Design Award Pláta Rehegua

Musiac Purahéi Ñe'ẽporã Mba'apoha ohupyty A' Design Award Pláta, peteĩ ñehechakuaa extraordinariamente prestigioso oñeme'ẽva diseño de primera línea, hechapyrãva ha impresionante-pe. Umi ganador Pláta ha'e tembiapo brillante ha heta creativo ohechaukáva expertise, talento ha agudeza sobresaliente umi diseñador-pe. Oñeha'ãrõ Musiac Purahéi Ñe'ẽporã Mba'apoha ha'eha peteĩ tembiapo maravilloso, ejemplar, profesional ha innovador pypukuvéva. Ko diseño ohupytýva A' Design Award Pláta ha'e invento ojehechakuaáva ha ojehayhúva ohepyme'ẽva ha ombohasáva límite arte, ciencia, diseño ha tecnología rehegua. Musiac Purahéi Ñe'ẽporã Mba'apoha ha'e creación heta ojeenvidiáva omokyre'ỹva ipotapy, ome'ẽva sentimiento positivo ha omoheñóiva maravilla ha sorpresa.

Diseño Rapykuéri

Marandu Aty

Diseñador ome'ẽva'ekue crédito, aty pegua ha mba'e técnico.

Mba'e Iñambuéva: 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.
Tuichakue ha Ñemyenyhẽ: 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.
Ñe'ẽ Rape: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Ñeapopy Tecnología: 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.
Diseño Jepy'apy: 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.
Ñesẽheka: 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.
Proyecto Ára Tuichakue: Musiac was a one year project done in 2018, Detroit.
Mba'apo Rape: 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.
Ta'ãnga Mba'eñemoneĩ: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.