Musiac Služba Hudobných Odporúčaní od Chia-Min Lin

Dizajn Musiac Služba Hudobných Odporúčaní od dizajnéra Chia-Min Lin je vysoko uznávaným víťazom veľmi prestížneho ocenenia 2020 Strieborná cena A' Design Award v súťažnej kategórii Rozhranie.

StriebornáRozhranie· 2020

Musiac Služba Hudobných Odporúčaní

od Chia-Min Lin

Klient: LinStudio

Víťaz ocenenia Strieborná cena A' Design Award v kategórii Rozhranie

57,305

Overený víťaz ocenenia

Overiť stav ocenenia pre #91014

Zobraziť na A' Design Award Tlačová správa Ocenenie – odznak

Farby

Dizajn

O dizajne Musiac Služba Hudobných Odporúčaní

Musiac je nástroj na odporúčanie hudby. Využívajú aktívnu účasť na hľadaní presných možností pre svojich používateľov. Jeho cieľom je navrhnúť alternatívne rozhrania na napadnutie autokracie algoritmu. Filtrovanie informácií sa stalo nevyhnutným vyhľadávacím prístupom. Vytvára však efekty ozveny a obmedzuje používateľov v ich zóne pohodlia striktným sledovaním ich preferencií. Používatelia sa stanú pasívnymi a prestanú sa pýtať na možnosti, ktoré zariadenie ponúka. Trávenie času na preskúmanie možností môže zvýšiť obrovské biologické náklady, je to však úsilie, ktoré vytvára zmysluplný zážitok.

Ocenenie

O ocenení Strieborná cena A' Design Award

Musiac Služba Hudobných Odporúčaní je laureát Striebornej ceny A' Design Award, mimoriadne prestížneho uznania udeľovaného nádherným, pôsobivým dizajnom najvyššej kvality. Laureáti Striebornej ceny sú nesmierne brilantné a mimoriadne kreatívne diela, ktoré ilustrujú výnimočnú odbornosť, talent a bystrosť svojich dizajnérov. Od Musiac Služba Hudobných Odporúčaní sa očakáva, že bude úžasným, vzorovým dielom, pozoruhodne profesionálnym a hlboko inovatívnym. Tento dizajn ocenený Striebornou cenou A' Design Award je veľmi pozoruhodný, obdivovaný vynález, ktorý prináša úžitok a posúva hranice umenia, vedy, dizajnu a technológie. Musiac Služba Hudobných Odporúčaní je výtvor, ktorému mnohí závidia, ktorý vzbudzuje túžby, prináša pozitívne pocity a vyvoláva úžas a údiv.

Za dizajnom

Tlačový kit

Kredity, členovia tímu a technické údaje poskytnuté dizajnérom.

Jedinečné vlastnosti: 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.
Rozmery a balenie: 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.
Štítky: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Výrobná technológia: 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.
Dizajnérska výzva: 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.
Výskum: 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.
Inšpirácia: 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.
Trvanie projektu: Musiac was a one year project done in 2018, Detroit.
Pracovný postup: 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.
Zdroje obrázkov: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.