Musiac Tšebeletso Ea Litlhahiso Tsa Mmino ka Chia-Min Lin

Moralo oa Musiac Tšebeletso Ea Litlhahiso Tsa Mmino ka Chia-Min Lin ke mohlodi oa khau e hlomphehang haholo ya 2020 Moputso oa Silevera oa A' Design Award Mokhahleong oa Tlhodisano oa Moralo wa Sebopeho, Tshebedisano le Boiphihlelo ba Mosebedisi.

SileveraSebopeho· 2020

Musiac Tšebeletso Ea Litlhahiso Tsa Mmino

ka Chia-Min Lin

Moreki: LinStudio

Mohlodi oa Moputso oa Silevera oa A' Design Award Mokhahleong oa Sebopeho

55,925

Mohlodi oa Khau ea Netefaditsweng

Netefatsa Boemo ba Khau bakeng sa #91014

Sheba ho A' Design Award Tokollo ya Boraditaba Baeche ya Moputso

Mebala

Moralo

Mabapi le Musiac Tšebeletso Ea Litlhahiso Tsa Mmino

Musiac ke enjene ea likeletso tsa 'mino, sebelisa tšebeliso ea maikutlo ho fumana khetho e nepahetseng bakeng sa basebelisi ba eona. E ikemiselitse ho fana ka maikutlo a mefuta e meng ho phephetsa matla a puso ea algorithm. Filter ea tlhahisoleseling e fetohile mokhoa o sa qojoang oa ho batla. Leha ho le joalo, e baka litlamorao tsa kamore ea echo mme e hatella basebelisi sebakeng sa bona sa matšeliso ka ho latela litakatso tsa bona ka mokhoa o hlakileng. Basebelisi ba iketsetsa borata ba emisa ho botsa lipotso tseo mochine o fanang ka tsona. Ho qeta nako ho hlahloba likhetho ho ka eketsa litšenyehelo tse kholo tsa bio, empa ke boiteko bo hlahisang boiphihlelo bo nang le morero.

Kamohelo

Mabapi le Moputso oa Silevera oa A' Design Award

Musiac Tšebeletso Ea Litlhahiso Tsa Mmino ke mohapi oa Moputso oa Silevera oa A' Design Award, kananelo e hlomphehang haholo e fuoang meralo e phahameng ka ho fetesisa, e ntle le e tsosang maikutlo. Bahapi ba Silevera ke mesebetsi e khanyang haholo le e nang le boqapi bo ikhethang e bontšang botsebi, talenta le bohlale ba baqapi ba eona. Musiac Tšebeletso Ea Litlhahiso Tsa Mmino e lebelletsoe ho ba mosebetsi o motle, oa mohlala, o nang le boprofeshenale bo bonahalang le mehopolo e mecha e tebileng. Moralo ona o hapileng Moputso oa Silevera oa A' Design Award ke mohopolo o hlokomelehang haholo le o ratoang o tlisang molemo mme o sutumelletsang meeli ea bonono, mahlale, moralo le theknoloji. Musiac Tšebeletso Ea Litlhahiso Tsa Mmino ke mosebetsi oo batho ba bangata ba o lakatsang, o tsosang litakatso, o tlisang maikutlo a matle le o tsosetsang ho makala le ho hlolla.

Ka Mora Moralo

Sete ya Boraditaba

Litlhaku, ditho tsa sehlopha le lintlha tsa botekgeniki tse fanoeng ke moetsi wa moralo.

Boleng bo Ikhethang: 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.
Boholo le Phuthelo: 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.
Lithegi: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Theknoloji ya Tlhahiso: 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.
Phephetso ya Moralo: 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.
Dipatlisiso: 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.
Kgothaletso: 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.
Nako ya Projeke: Musiac was a one year project done in 2018, Detroit.
Tsamaiso ya Mosebetsi: 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.
Litlotla tsa Setshwantsho: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.