Musiac Glasbena Priporočila Storitev – avtor Chia-Min Lin

Delo Musiac Glasbena Priporočila Storitev avtorja Chia-Min Lin je izjemno cenjeni dobitnik prestižne nagrade 2020 Srebrna nagrada A' Design Award v tekmovalni kategoriji Oblikovanje vmesnikov, interakcij in uporabniške izkušnje.

SrebrnaVmesnik· 2020

Musiac Glasbena Priporočila Storitev

avtor/ica: Chia-Min Lin

Naročnik: LinStudio

Dobitnik nagrade Srebrna nagrada A' Design Award v kategoriji Vmesnik

57,305

Potrjeni dobitnik nagrade

Preverite status nagrade za #91014

Ogled na A' Design Award Sporočilo za javnost Značka nagrade

Barve

Dizajn

O dizajnu Musiac Glasbena Priporočila Storitev

Musiac je glasbeno priporočilo, s pomočjo proaktivne udeležbe poišče natančne možnosti za svoje uporabnike. Njegov namen je predlagati alternativne vmesnike, s katerimi bi izzvali algoritem avtokracije. Filtriranje informacij je postalo neizogiben iskalni pristop. Vendar ustvarja učinke komornih odmevov in omeji uporabnike v svojem območju udobja, tako da strogo upoštevajo njihove želje. Uporabniki postanejo pasivni in nehajo dvomiti v možnosti, ki jih ponuja stroj. Poraba časa za pregled možnosti lahko poveča velike bio-stroške, vendar je to napor, ki ustvarja smiselno izkušnjo.

Priznanje

O nagradi Srebrna nagrada A' Design Award

Musiac Glasbena Priporočila Storitev je prejemnik Srebrne nagrade A' Design Award, izjemno prestižnega priznanja, ki se podeljuje čudovitim, impresivnim vrhunskim oblikovanjem. Prejemniki Srebrne nagrade so izjemno briljantna in zelo ustvarjalna dela, ki ponazarjajo izjemno strokovnost, talent in bistrost svojih oblikovalcev. Od Musiac Glasbena Priporočila Storitev se pričakuje, da bo čudovito, zgledno delo, opazno profesionalno in globoko inovativno. To oblikovanje, nagrajeno s Srebrno nagrado A' Design Award, je zelo opazen, občudovan izum, ki koristi in premika meje umetnosti, znanosti, oblikovanja in tehnologije. Musiac Glasbena Priporočila Storitev je stvaritev, ki ji mnogi zavidajo, ki prebuja želje, prinaša pozitivna čustva in vzbuja začudenje in občudovanje.

V ozadju dizajna

Komplet za medije

Zasluge, člani ekipe in tehnične podrobnosti, ki jih je posredoval oblikovalec.

Edinstvene lastnosti: 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.
Dimenzije in embalaža: 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.
Oznake: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Proizvodna tehnologija: 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.
Oblikovalski izziv: 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.
Raziskave: 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.
Inspiracija: 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.
Trajanje projekta: Musiac was a one year project done in 2018, Detroit.
Potek delovanja: 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.
Zasluge za slike: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.