Musiac Хадамоти Тавсиядиҳии Мусиқӣ аз Chia-Min Lin

Тарҳи Musiac Хадамоти Тавсиядиҳии Мусиқӣ аз Chia-Min Lin ғолиби шоистаи ҷоизаи бонуфузи 2020 Ҷоизаи нуқрагии A' Design Award дар категорияи мусобиқавии Дизайни интерфейс, интеракция ва таҷрибаи корбар мебошад.

НуқраИнтерфейс· 2020

Musiac Хадамоти Тавсиядиҳии Мусиқӣ

аз ҷониби Chia-Min Lin

Муштарӣ: LinStudio

Ғолиби Ҷоизаи нуқрагии A' Design Award дар категорияи Интерфейс

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Ғолиби тасдиқшудаи ҷоиза

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Дар A' Design Award дидан Релизи матбуотӣ Нишони ҷоиза

Рангҳо

Тарҳ

Дар бораи Musiac Хадамоти Тавсиядиҳии Мусиқӣ

Musiac муҳаррики тавсиядиҳии мусиқӣ мебошад, ки иштироки фаъолро барои дарёфт кардани имконоти дақиқ барои корбарони худ истифода баред. Ҳадафи он пешниҳод кардани интерфейсҳои алтернативӣ барои эътироз ба алгоритми автократия мебошад. Филтри маълумот ба равиши ногузири ҷустуҷӯ табдил ёфтааст. Бо вуҷуди ин, он эффектҳои камераро эҷод мекунад ва корбаронро дар минтақаи тасаллии худ бо роҳи қатъӣ аз рӯи афзалиятҳои худ маҳдуд месозад. Истифодабарандагон ғайрифаъол мешаванд ва саволҳои имконпазири мошини додашударо қатъ мекунанд. Сарфи вақтро барои азназаргузаронии опсияҳо метавонад хароҷоти калони биологиро афзун кунад, аммо ин талошест, ки таҷрибаи муҳимро ба вуҷуд меорад.

Эътироф

Дар бораи Ҷоизаи нуқрагии A' Design Award

Musiac Хадамоти Тавсиядиҳии Мусиқӣ ғолиби Ҷоизаи нуқрагии A' Design Award аст, ки эътирофи фавқулодда обрӯманд буда, ба тарҳҳои сатҳи олӣ, зебо ва таъсирбахш дода мешавад. Ғолибони нуқра асарҳои бағоят дурахшон ва бениҳоят эҷодкоронаанд, ки таҷрибаи барҷаста, истеъдод ва зиракии тарроҳони худро нишон медиҳанд. Интизор меравад, ки Musiac Хадамоти Тавсиядиҳии Мусиқӣ асари аҷоиб ва намунавӣ бошад, ки ба таври назаррас касбӣ ва амиқан навоваронаанд. Ин тарҳи ғолиби Ҷоизаи нуқрагии A' Design Award ихтирои хеле назаррас ва мавриди таҳсин аст, ки судманд аст ва ҳудуди санъат, илм, тарроҳӣ ва технологияро пеш мебарад. Musiac Хадамоти Тавсиядиҳии Мусиқӣ офаридаест, ки бисёриҳо ба он ҳасад мебаранд ва хостаҳоро бедор мекунад, эҳсосоти мусбат меорад ва ҳайрат ва тааҷҷуб ба вуҷуд меорад.

Дар паси тарҳ

Маҷмӯаи матбуотӣ

Кредитҳо, аъзои гурӯҳ ва тафсилоти техникие, ки тарроҳ пешниҳод кардааст.

Хусусиятҳои беназир: 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.
Андоза ва бастабандӣ: 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.
Тегҳо: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Технологияи истеҳсол: 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.
Мушкилоти дизайн: 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.
Таҳқиқот: 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.
Илҳом: 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.
Давомнокии лоиҳа: Musiac was a one year project done in 2018, Detroit.
Ҷараёни кор: 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.
Ҳуқуқи тасвирҳо: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.