Musiac Muzika Rekomendoservo de Chia-Min Lin

La dizajno Musiac Muzika Rekomendoservo de Chia-Min Lin estas tre estimata gajninto de la prestiĝa 2020 Arĝenta A' Design Award en la Konkursa Kategorio Dezajno de Interfaco, Interago kaj Uzantsperto.

ArĝentoInterfaco· 2020

Musiac Muzika Rekomendoservo

de Chia-Min Lin

Kliento: LinStudio

Gajninto de Arĝenta A' Design Award en la Kategorio Interfaco

57,305

Kontrolita Premiogajninto

Kontroli Premiostaton por #91014

Vidi ĉe A' Design Award Gazetara Komuniko Premia Insigno

Koloroj

La Dezajno

Pri Musiac Muzika Rekomendoservo

Musiac estas muzika rekomendilo, uzi porpacan partoprenon por trovi precizajn eblojn por siaj uzantoj. Ĝi celas proponi alternativajn interfacojn por defii la algoritman aŭtokration. Informfiltrado fariĝis nepra serĉa aliro. Tamen ĝi kreas e chamberajn ĉambrajn efikojn kaj limigas uzantojn en sia komforta zono strikte sekvante siajn preferojn. Uzantoj iĝas pasivaj kaj ĉesas pridemandi la elektojn, kiujn la maŝino donas. Elspezi tempon por revizii eblojn povas pliigi grandegan bio-koston, sed ĝi klopodas krei signifan sperton.

La Agnosko

Pri la Arĝenta A' Design Award

Musiac Muzika Rekomendoservo estas gajninto de la Arĝenta A' Design Award, grandege prestiĝa rekono donata al plejklasaj, belegaj kaj impresaj dezajnoj. La Arĝentaj gajnintoj estas treege brilaj kaj treege kreemaj verkoj, kiuj ilustras la elstaran sperton, talenton kaj sagacon de siaj dezajnistoj. Oni atendas, ke Musiac Muzika Rekomendoservo estu mirinda, montrinda verko, kiu estas rimarkinde profesia kaj profunde nova. Ĉi tiu dezajno gajninta la Arĝentan A' Design Award estas tre rimarkinda, admirata inventaĵo, kiu profitigas kaj antaŭenigas la limojn de arto, scienco, dezajno kaj teknologio. Musiac Muzika Rekomendoservo estas vaste enviata kreaĵo, kiu vekas dezirojn, alportas pozitivajn sentojn kaj ekbrilas miron kaj mirigon.

Malantaŭ la Dezajno

Gazetara Dosiero

Kreditoj, teamanoj kaj teknikaj detaloj provizitaj de la dezajnisto.

Unikaj Ecoj: 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.
Dimensioj kaj Pakado: 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.
Etikedoj: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Produkta Teknologio: 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.
Dizajna Defio: 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.
Esplorado: 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.
Inspiro: 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.
Projekta Daŭro: Musiac was a one year project done in 2018, Detroit.
Funkcia Fluo: 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.
Bildaj Kreditoj: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.