Musiac ʻ Servicelelo Lawelawe Loiloi na Chia-Min Lin

ʻO Musiac ʻ Servicelelo Lawelawe Loiloi na Chia-Min Lin he mea lanakila hanohano loa o ka 2020 A' Design Award Kala ma ka Māhele Hoʻokūkū Hoʻolālā Pilina, Launa a me ʻIke Mea Hoʻohana.

KalaPilina· 2020

Musiac ʻ Servicelelo Lawelawe Loiloi

na Chia-Min Lin

Mea Kūʻai: LinStudio

Mea Lanakila A' Design Award Kala ma ka Māhele Pilina

57,305

Mea Lanakila i Hōʻoia ʻIa

Hōʻoia i ke Kūlana Makana no #91014

Nānā ma A' Design Award Hoʻolaha Nūhou Hōʻailona Makana

Nā Waihoʻoluʻu

Ka Hoʻolālā

E Pili Ana iā Musiac ʻ Servicelelo Lawelawe Loiloi

ʻO Musiac kahi ʻōnaehana o ka puʻuwai mele, hoʻohana i ka hoʻoulu ʻia e loaʻa i nā koho kūpono no nā mea hoʻohana. Hoʻōla ia i kahi kikoʻī aʻe i nā kaohi e hoʻoweliweli i ka autocracy algorithm. ʻO ka kānana ʻikepili i lilo i kahi ala huli ʻole ʻole. Eia nō naʻe, hana ia i nā hopena kāʻei o ka echo a hoʻopaʻa i nā mea hoʻohana i ko lākou ʻoluʻolu hōʻoluʻolu e ke kahakaha i ko lākou makemake. E lilo nā mea hoʻohana i ka passive a pani i ka nīnau i nā koho i hāʻawi ʻia e ka mīkini. Mālama ka manawa e nānā ai i nā koho e hoʻonui i ka nui o ke kumukūʻai bio, akā ʻo ka hoʻoikaika ia e hana ai i kahiʻike kūpono.

Ka Hoʻomaikai

E Pili Ana i ka A' Design Award Kala

Ua lanakila ka hoʻolālā Musiac ʻ Servicelelo Lawelawe Loiloi i ka A' Design Award Kala, he ʻike hoʻohanohano nui loa i hāʻawi ʻia i nā hoʻolālā kiʻekiʻe loa, nani a kupaianaha. He mau hana ʻālohilohi loa a hana noʻeau nui nā lanakila Kala e hōʻike ana i ka loea, ka makakoho a me ka ʻoi aku o ka noʻonoʻo o kā lākou poʻe hoʻolālā. Manaʻo ʻia ʻo ka hoʻolālā Musiac ʻ Servicelelo Lawelawe Loiloi he hana kupaianaha, kūlana ʻoi aku, ʻoihana kiʻekiʻe a hou loa. ʻO kēia hoʻolālā i lanakila i ka A' Design Award Kala he mea hoʻokumu kūpono loa, mahalo ʻia e hāʻawi i ka pōmaikaʻi a hoʻoneʻe i nā palena o ka hana noʻeau, ka ʻepekema, ka hoʻolālā a me ka ʻenehana. He mea i ʻeleu ʻia e nā mea he nui ka hoʻolālā Musiac ʻ Servicelelo Lawelawe Loiloi e hoʻāla i ka makemake, lawe mai i nā manaʻo maikaʻi a hoʻāla i ka kamahaʻo a me ka mahalo.

Ma Hope o ka Hoʻolālā

ʻEke Nūhou

Nā hōʻoia, nā lālā hui a me nā kikoʻī ʻenehana i hāʻawi ʻia e ka mea hoʻolālā.

Nā ʻAno Kūʻokoʻa: 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.
Nā Ana a me ka Hoʻopili: 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.
Nā Lepili: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
ʻEnehana Hana: 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.
Pilikia Hoʻolālā: 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.
Noiʻi: 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.
Hoʻoulu Manaʻo: 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.
Ka Lōʻihi o ka Papahana: Musiac was a one year project done in 2018, Detroit.
Kaʻina Hana: 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.
Nā Kuleana Kiʻi: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.