Disainilahendus Musiac Muusikasoovitusteenus, autor Chia-Min Lin

Disainilahendus Musiac Muusikasoovitusteenus, mille autor on Chia-Min Lin, on kõrgelt hinnatud 2020 auhinna Hõbeauhind A' Design Award võitja konkursi kategoorias Kasutajaliidese, interaktsiooni ja kasutajakogemuse disain.

HõbeKasutajaliides· 2020

Musiac Muusikasoovitusteenus

disainer: Chia-Min Lin

Klient: LinStudio

Auhind Hõbeauhind A' Design Award kategoorias Kasutajaliides

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Teosest Musiac Muusikasoovitusteenus

Musiac on muusikaline soovitusmootor, kasutage ennetavat osalemist, et leida oma kasutajatele täpsed võimalused. Selle eesmärk on pakkuda välja alternatiivseid liideseid, et vaidlustada algoritmi autokraatia. Teabe filtreerimine on muutunud vältimatuks otsimisviisiks. Kuid see loob kajakambriefektid ja piirab kasutajaid nende mugavustsoonis, järgides rangelt nende eelistusi. Kasutajad muutuvad passiivseteks ja lõpetavad masina pakutavate võimaluste kahtluse alla seadmise. Kulutades aega võimaluste ülevaatamiseks, võib tõusta tohutuid biokulusid, kuid just pingutused loovad sisuka kogemuse.

Tunnustus

Auhinnast Hõbeauhind A' Design Award

Musiac Muusikasoovitusteenus on Hõbeauhinna A' Design Award võitja, ülimalt prestiižikas tunnustus, mis antakse tipptasemel, suurepärastele ja muljetavaldavatele disainidele. Hõbevõitjad on ülimalt säravad ja erakordselt loovad tööd, mis illustreerivad oma disainerite silmapaistvat kogemust, annet ja teravmeelsust. Musiac Muusikasoovitusteenus peaks olema imeline, eeskujulik töö, mis on märkimisväärselt professionaalne ja sügavalt uuenduslik. See Hõbeauhinna A' Design Award võidudisain on väga tähelepanuväärne, imetletud leiutis, mis toob kasu ning nihutab kunsti, teaduse, disaini ja tehnoloogia piire. Musiac Muusikasoovitusteenus on laialdaselt kadestatud looming, mis äratab soove, tekitab positiivseid tundeid ning süütab hämmastust ja imestust.

Disaini taustal

Pressimaterjalid

Disaineri esitatud autorid, meeskonnaliikmed ja tehnilised andmed.

Unikaalsed omadused: 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.
Mõõtmed ja pakend: 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.
Sildid: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Tootmistehnoloogia: 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.
Disainiülesanne: 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.
Uurimistöö: 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.
Inspiratsioon: 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.
Projekti kestus: Musiac was a one year project done in 2018, Detroit.
Tööprotsess: 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.
Piltide autorid: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.