Musiac Iṣẹ Iṣeduro Orin láti ọwọ́ Chia-Min Lin

Musiac Iṣẹ Iṣeduro Orin láti ọwọ́ Chia-Min Lin jẹ́ aṣẹ́gun olókìkí púpọ̀ ti 2020 Àmì-ẹ̀yẹ Fàdákà A' Design Award nínú Ẹ̀ka Ìdíje Àpẹẹrẹ Ojú-ìbáṣepọ̀, Ìbáṣepọ̀ àti Ìrírí Olùlò.

FàdákàOjú-ìbáṣepọ̀· 2020

Musiac Iṣẹ Iṣeduro Orin

látọ̀wọ́ Chia-Min Lin

Oníbàárà: LinStudio

Aṣẹ́gun Àmì-ẹ̀yẹ Fàdákà A' Design Award nínú Ẹ̀ka Ojú-ìbáṣepọ̀

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Aṣẹ́gun Àmì Ẹ̀yẹ Tí a Jẹ́rìísí

Jẹ́rìísí Ipò Àmì Ẹ̀yẹ fún #91014

Wo ní A' Design Award Ìwé Ìròyìn Àmì Ẹ̀yẹ

Àwọ̀

Àpẹrẹ Náà

Nípa Musiac Iṣẹ Iṣeduro Orin

Musiac jẹ ẹrọ ṣiṣe iṣeduro orin, lo ikopa iṣakora lati wa awọn aṣayan kongẹ fun awọn olumulo rẹ. O ṣe ifọkansi lati ṣalaye awọn atọka omiiran lati dojuko algorithm autocracy. Ṣiṣe alaye ifilọlẹ ti di ọna wiwa-laigbaṣe. Sibẹsibẹ, o ṣẹda awọn ipa iwo oju iyẹwu ati mu awọn olumulo ṣiṣẹ ni agbegbe itunu wọn nipa titọ awọn ifẹ wọn. Awọn olumulo di palolo ati da duro lere awọn aṣayan ti ẹrọ n pese. Lilo akoko lati ṣe ayẹwo awọn aṣayan le mu iye-owo nla ti iye-nla pọ si, ṣugbọn o jẹ igbiyanju ti o ṣẹda iriri ti o nilari.

Ìdánimọ̀ Náà

Nípa Àmì-ẹ̀yẹ Fàdákà A' Design Award

Musiac Iṣẹ Iṣeduro Orin jẹ́ olùborí Àmì-ẹ̀yẹ Fàdákà A' Design Award, ìdánimọ̀ onípọ̀nlé lọ́nà àrà ọ̀tọ̀ tí a fún àwọn àwòṣe ìpele gíga jùlọ tó dára tó sì ń kan ọkàn. Àwọn olùborí Fàdákà jẹ́ àwọn iṣẹ́ tó ń tàn yòò gidigidi tó sì ní ìṣẹ̀dá púpọ̀ tí wọ́n ń ṣàfihàn ìmọ̀ ìjìnlẹ̀, ẹ̀bùn àti ìmọ̀ràn àwọn olùṣàwòṣe wọn. A retí pé Musiac Iṣẹ Iṣeduro Orin yóò jẹ́ iṣẹ́ àgbàyanu, àpẹẹrẹ rere, ti ọ̀jáfáfá lọ́nà tó ṣe pàtàkì tó sì ní ìmúṣẹ̀dá tuntun jinlẹ̀. Àwòṣe tó borí Àmì-ẹ̀yẹ Fàdákà A' Design Award yìí jẹ́ ohun ìmúṣẹ̀dá tó hàn gbangba gidigidi tí a mọrírì tó ń ṣe anfàní tó sì ń tì ààlà iṣẹ́ ọnà, sáyẹ́ǹsì, àwòṣe àti ìmọ̀-ẹ̀rọ síwájú. Musiac Iṣẹ Iṣeduro Orin jẹ́ ẹ̀dá tí ọ̀pọ̀lọpọ̀ ń ṣe ìlara, tó ń jí ìfẹ́ inú, tó ń mú ìmọ̀lára rere wá tó sì ń jí ìyàlẹ́nu àti ìyanu.

Lẹ́yìn Àpẹrẹ Náà

Àkójọ Ìròyìn

Àwọn ẹ̀tọ́, àwọn ọmọ ẹgbẹ́ àti àlàyé ìmọ̀ ẹ̀rọ tí olùṣàpẹrẹ pèsè.

Àwọn Ohun Àkànṣe: 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.
Ìwọ̀n àti Àpò: 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.
Àwọn Àkọlé: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Ìmọ̀-ẹ̀rọ Ìṣelọ́pọ̀: 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.
Ìpèníjà Àpèsè: 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.
Ìwádìí: 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.
Ìmísí: 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.
Àkókò Iṣẹ́ Àkànṣe: Musiac was a one year project done in 2018, Detroit.
Ìlànà Ìṣiṣẹ́: 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.
Ìfọwọ́sí Àwòrán: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.