Musiac Perkhidmatan Cadangan Muzik oleh Chia-Min Lin

Reka bentuk Musiac Perkhidmatan Cadangan Muzik oleh Chia-Min Lin ialah pemenang yang sangat dihormati bagi 2020 A' Design Award Perak yang amat berprestij dalam Kategori Pertandingan Reka Bentuk Antara Muka, Interaksi dan Pengalaman Pengguna.

PerakAntara Muka· 2020

Musiac Perkhidmatan Cadangan Muzik

oleh Chia-Min Lin

Klien: LinStudio

Pemenang A' Design Award Perak dalam Kategori Antara Muka

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Pemenang Anugerah Yang Disahkan

Sahkan Status Anugerah untuk #91014

Lihat di A' Design Award Siaran Akhbar Lencana Anugerah

Warna

Reka Bentuk

Mengenai Musiac Perkhidmatan Cadangan Muzik

Musiac adalah mesin saran muzik, gunakan penyertaan proaktif untuk mencari pilihan tepat bagi penggunanya. Ini bertujuan untuk mencadangkan antara muka alternatif untuk mencabar autokrasi algoritma. Penyaringan maklumat telah menjadi pendekatan pencarian yang tidak dapat dielakkan. Walau bagaimanapun, ia mewujudkan kesan ruang gema dan membatasi pengguna di zon selesa mereka dengan mengikut pilihan mereka dengan tegas. Pengguna menjadi pasif dan berhenti mempersoalkan pilihan yang disediakan mesin. Menghabiskan masa untuk meninjau pilihan dapat meningkatkan kos bio yang besar, tetapi usaha itulah yang menghasilkan pengalaman yang bermakna.

Pengiktirafan

Mengenai A' Design Award Perak

Musiac Perkhidmatan Cadangan Muzik ialah pemenang A' Design Award Perak, pengiktirafan yang sangat berprestij yang dianugerahkan kepada reka bentuk bertaraf tinggi, indah dan mengagumkan. Pemenang Perak ialah karya yang amat cemerlang dan sangat kreatif yang menggambarkan kepakaran, bakat dan ketajaman fikiran yang luar biasa pereka bentuknya. Musiac Perkhidmatan Cadangan Muzik diharapkan menjadi karya yang menakjubkan, contoh teladan, sangat profesional dan sangat inovatif. Reka bentuk pemenang A' Design Award Perak ini ialah ciptaan yang sangat ketara dan dikagumi yang memberi manfaat kepada dan memajukan batas seni, sains, reka bentuk dan teknologi. Musiac Perkhidmatan Cadangan Muzik ialah ciptaan yang sangat dicemburui yang membangkitkan keinginan, membawa perasaan positif dan mencetuskan rasa hairan serta kagum.

Di Sebalik Reka Bentuk

Kit Akhbar

Kredit, ahli pasukan dan butiran teknikal yang disediakan oleh pereka.

Ciri-Ciri Unik: 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.
Dimensi & Pembungkusan: 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.
Tag: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Teknologi Pengeluaran: 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.
Cabaran Reka Bentuk: 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.
Penyelidikan: 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.
Inspirasi: 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.
Tempoh Projek: Musiac was a one year project done in 2018, Detroit.
Aliran Operasi: 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.
Kredit Imej: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.