Musiac Ko Te Ratonga Tūtohutanga Puoro nā Chia-Min Lin

Ko te hoahoa Musiac Ko Te Ratonga Tūtohutanga Puoro nā Chia-Min Lin he kaitoa whakahirahira o te 2020 A' Design Award Hiriwa i te Wāhanga Whakataetae Hoahoa Atanga, Tauwhitiwhiti me te Wheako Kaiwhakamahi.

HiriwaAtanga· 2020

Musiac Ko Te Ratonga Tūtohutanga Puoro

nā Chia-Min Lin

Kiritaki: LinStudio

Kaitoa o te A' Design Award Hiriwa i te Wāhanga Atanga

57,305

Kaitoa Tohu Manatokohia

Manatokohia te Tūnga Tohu mō #91014

Tirohia i te A' Design Award Pānui Pāpāho Tohu Tohu

Ngā Tae

Te Hoahoa

Mō Musiac Ko Te Ratonga Tūtohutanga Puoro

Ko Musiac he miihini tūtohutanga puoro, whakamahia te whaiwhaitanga ki te rapu i nga whiringa tika mo ona kaiwhakamahi. Ko te hiahia ki te tuku i nga hononga taapiri hei wero i te torangapu algorithm. Ko te tātaritanga korero hei huarahi rapu koreutu. Heoi, ka waihohia e nga wharenui ahumoana nga huanga ka whakapau kaha i nga kaiwhakamahi ki te rohe whakamarie ma te whai i o raatau hiahia. Ka uru nga kaiwhakamahi me te aukati i nga paatai ka tohua e te miihini. Ko te whakapaunga i te waa ki te arotake i nga whiringa ka nui ake te utu mo te utu koiora, engari ko te mahi tera ka puta he wheako whai kiko.

Te Whakamihi

Mō te A' Design Award Hiriwa

Ko Musiac Ko Te Ratonga Tūtohutanga Puoro te toa o te A' Design Award Hiriwa, he whakaaetanga tino whai mana e hoatu ana ki ngā hoahoa taumata teitei rawa, ātaahua me te whakamīharo. Ko ngā toa Hiriwa he mahi tino kanapa, tino auaha e whakaatu ana i te mātanga, te pūmanawa me te mōhio hōhonu o ā rātou kaihoahoa. E tūmanakohia ana ko Musiac Ko Te Ratonga Tūtohutanga Puoro he mahi whakamīharo, he tauira, he tino ngaio, he auaha hōhonu. Ko tēnei hoahoa i toa i te Hiriwa he hanganga rongonui, whakamīharotia e whai wāhi ana ki te whakawhānui i ngā rohe o te toi, te pūtaiao, te hoahoa me te hangarau. Ko Musiac Ko Te Ratonga Tūtohutanga Puoro he mahi e hae ana te tini, e whakaara ana i ngā hiahia, e kawe mai ana i ngā kare-ā-roto pai, ā, e whakaoho ana i te mīharo.

I Muri i te Hoahoa

Kete Pāpāho

Ngā whakamihi, ngā mema o te rōpū me ngā taipitopito hangarau i whakaratohia e te kaihoahoa.

Āhuatanga Motuhake: 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.
Inenga & Pēketanga: 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.
Tūtohu: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Hangarau Whakanao: 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.
Wero Hoahoa: 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.
Rangahau: 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.
Whakaaweawe: 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.
Roanga o te Kaupapa: Musiac was a one year project done in 2018, Detroit.
Rerenga Mahi: 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.
Whakamana Whakaahua: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.