Musiac Musiekaanbevelingsdiens deur Chia-Min Lin

Die ontwerp Musiac Musiekaanbevelingsdiens deur Chia-Min Lin is 'n hoogs gewaardeerde wenner van die baie prestigieuese 2020 A' Design Award in Silwer in die Koppelvlak-, Interaksie- en Gebruikerservaring-ontwerp-kompetisiekategorie.

SilwerKoppelvlak· 2020

Musiac Musiekaanbevelingsdiens

deur Chia-Min Lin

Kliënt: LinStudio

Wenner van die A' Design Award in Silwer in die Koppelvlak-kategorie

57,305

Geverifieerde Toekenningswenner

Verifieer Toekenningsstatus vir #91014

Bekyk by A' Design Award Persverklaring Toekenningskenteken

Kleure

Die Ontwerp

Oor Musiac Musiekaanbevelingsdiens

Musiac is 'n musikale aanbeveling enjin, gebruik proaktiewe deelname om presiese opsies vir sy gebruikers te vind. Dit het ten doel om alternatiewe koppelvlakke voor te stel om die outokrasie van algoritme uit te daag. Inligtingfiltrering het 'n onvermydelike soekbenadering geword. Dit skep egter eggokamereffekte en beperk gebruikers in hul gemaksone deur hul voorkeure streng te volg. Gebruikers raak passief en hou op om die opsies wat die masjien bied te bevraagteken. As u tyd spandeer om opsies te hersien, kan dit baie bio-koste verhoog, maar dit is die poging wat 'n betekenisvolle ervaring skep.

Die Erkenning

Oor die A' Design Award in Silwer

Musiac Musiekaanbevelingsdiens is 'n wenner van die A' Design Award in Silwer, 'n buitengewoon prestigieuse erkenning wat toegeken word aan topklas-, pragtige en indrukwekkende ontwerpe. Wenners in Silwer is geweldig briljante en uiters kreatiewe werke wat die uitstaande kundigheid, talent en skerpsinnigheid van hul ontwerpers illustreer. Daar word verwag dat Musiac Musiekaanbevelingsdiens 'n wonderlike vertoonstuk is wat merkwaardig professioneel en diepgaande innoverend is. Hierdie ontwerp wat die A' Design Award in Silwer gewen het, is 'n hoogs noemenswaardige, bewonderde uitvinding wat die grense van kuns, wetenskap, ontwerp en tegnologie bevoordeel en uitbrei. Musiac Musiekaanbevelingsdiens is 'n wyd beny skepping wat begeertes oproep, positiewe gevoelens bring en verbasing en verwondering wek.

Agter die Ontwerp

Persstel

Krediete, spanlede en tegniese besonderhede verskaf deur die ontwerper.

Unieke Eienskappe: 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.
Afmetings & Verpakking: 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.
Etikette: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Produksietegnologie: 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.
Ontwerpuitdaging: 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.
Navorsing: 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.
Inspirasie: 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.
Projekduur: Musiac was a one year project done in 2018, Detroit.
Bedryfsvloei: 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.
Beeldkrediete: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.