Musiac Mūzikas Ieteikumu Pakalpojums — Chia-Min Lin

Darbs Musiac Mūzikas Ieteikumu Pakalpojums, kura autors ir Chia-Min Lin, ir augsti novērtēts un ļoti prestižās 2020 Sudraba balva A' Design Award ieguvējs kategorijā Saskarnes, mijiedarbības un lietotāja pieredzes dizains.

SudrabaSaskarne· 2020

Musiac Mūzikas Ieteikumu Pakalpojums

Autors: Chia-Min Lin

Klients: LinStudio

Sudraba balva A' Design Award ieguvējs kategorijā Saskarne

57,305

Verificēts balvas ieguvējs

Verificēt balvas statusu dizainam #91014

Skatīt A' Design Award Preses relīze Balvas žetons

Krāsas

Dizains

Par Musiac Mūzikas Ieteikumu Pakalpojums

Musiac ir muzikāls ieteikumu dzinējs, izmantojiet aktīvu līdzdalību, lai atrastu precīzas iespējas tā lietotājiem. Tā mērķis ir ierosināt alternatīvas saskarnes, lai izaicinātu algoritma autokrātiju. Informācijas filtrēšana ir kļuvusi par neizbēgamu meklēšanas metodi. Tomēr tas rada atbalss kameras efektus un ierobežo lietotājus viņu komforta zonā, stingri ievērojot viņu vēlmes. Lietotāji kļūst pasīvi un pārtrauc apšaubīt mašīnas piedāvātās iespējas. Pavadot laiku, lai pārskatītu iespējas, var pieaugt milzīgas bioizmaksas, bet tieši centieni rada jēgpilnu pieredzi.

Atzinība

Par Sudraba balva A' Design Award

Musiac Mūzikas Ieteikumu Pakalpojums ir Sudraba balvas A' Design Award ieguvējs, ārkārtīgi prestiža atzinība, ko piešķir augstākās klases, krāšņiem un iespaidīgiem dizainiem. Sudraba ieguvēji ir ārkārtīgi spožas un ļoti radošas izpausmes, kas parāda to dizaineru izcilo kompetenci, talantu un asredzību. Sagaidāms, ka Musiac Mūzikas Ieteikumu Pakalpojums būs lielisks, paraugdarbs, ļoti profesionāls un dziļi inovatīvs. Šis ar Sudraba balvu apbalvotais dizains ir ievērojams, apbrīnots izgudrojums, kas palīdz paplašināt mākslas, zinātnes, dizaina un tehnoloģiju robežas. Musiac Mūzikas Ieteikumu Pakalpojums ir radījums, kam daudzi apskauž, kas modina vēlmes, rada pozitīvas izjūtas un raisa izbrīnu un apbrīnu.

Aiz dizaina

Preses materiāli

Dizainera sniegtie autori, komandas dalībnieki un tehniskie dati.

Unikālās īpašības: 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.
Izmēri un iepakojums: 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.
Birkas: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Ražošanas tehnoloģija: 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.
Dizaina izaicinājums: 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.
Pētījums: 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.
Iedvesma: 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 ilgums: Musiac was a one year project done in 2018, Detroit.
Darbības plūsma: 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.
Attēlu autori: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.