Musiac Muzikos Rekomendacijų Paslauga – autorius Chia-Min Lin

Darbas Musiac Muzikos Rekomendacijų Paslauga, kurio autorius Chia-Min Lin, yra itin vertinamas prestižinio 2020 Sidabrinis apdovanojimas A' Design Award laimėtojas konkurso kategorijoje Sąsajos, sąveikos ir naudotojo patirties dizainas.

SidabrinisSąsaja· 2020

Musiac Muzikos Rekomendacijų Paslauga

Dizaineris: Chia-Min Lin

Klientas: LinStudio

Kategorijos Sąsaja laimėtojas – Sidabrinis apdovanojimas A' Design Award

57,305

Patvirtintas apdovanojimo laimėtojas

Patikrinti apdovanojimo statusą – #91014

Peržiūrėti A' Design Award Pranešimas spaudai Apdovanojimo ženklelis

Spalvos

Dizainas

Apie Musiac Muzikos Rekomendacijų Paslauga

„Musiac“ yra muzikinis rekomendacijų variklis, naudokite aktyvų dalyvavimą ieškant tikslių variantų jo vartotojams. Ja siekiama pasiūlyti alternatyvių sąsajų, kad būtų galima užginčyti algoritmo autokratiškumą. Informacijos filtravimas tapo neišvengiamu paieškos metodu. Tačiau tai sukuria aido kameros efektus ir suvaržo vartotojus jų komforto zonoje, griežtai laikydamiesi jų nuostatų. Vartotojai tampa pasyvūs ir nustoja abejoti mašinos teikiamomis galimybėmis. Praleidę laiką galimybių peržiūrai, gali padidėti didžiulės biologinės išlaidos, tačiau pastangos sukuria prasmingą patirtį.

Pripažinimas

Apie Sidabrinis apdovanojimas A' Design Award

Musiac Muzikos Rekomendacijų Paslauga laimėjo Sidabrinį apdovanojimą A' Design Award, nepaprastai prestižinį pripažinimą, skiriamą aukščiausios klasės, puikiems ir įspūdingiems dizainams. Sidabro laureatai yra itin puikūs ir labai kūrybingi darbai, rodantys jų kūrėjų išskirtinę kompetenciją, talentą ir įžvalgumą. Tikimasi, kad Musiac Muzikos Rekomendacijų Paslauga bus nuostabus, pavyzdinis, labai profesionalus ir giliai novatoriškas kūrinys. Šis Sidabriniu apdovanojimu įvertintas dizainas yra žymus, žavimas išradimas, padedantis plėsti meno, mokslo, dizaino ir technologijų ribas. Musiac Muzikos Rekomendacijų Paslauga yra kūrinys, kuriam daugelis pavydi, žadinantis troškimus, teikiantis teigiamų jausmų ir sukeliantis nuostabą bei stebėjimąsi.

Už dizaino

Spaudos rinkinys

Dizainerio pateikti kreditai, komandos nariai ir techninė informacija.

Unikalios savybės: 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.
Matmenys ir pakuotė: 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.
Žymos: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
Gamybos technologija: 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.
Dizaino iššūkis: 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.
Tyrimas: 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.
Įkvėpimas: 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.
Projekto trukmė: Musiac was a one year project done in 2018, Detroit.
Veikimo eiga: 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.
Nuotraukų autoriai: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.