Chia-Min Lin طرفان Musiac موسيقي جي سفارش سروس

Chia-Min Lin طرفان Musiac موسيقي جي سفارش سروس انتهائي معتبر 2020 A' Design Award چاندي انعام جو فاتح آهي انٽرفيس، تعامل ۽ صارف تجربو ڊيزائن مقابلي جي درجي ۾.

چانديانٽرفيس· 2020

Musiac موسيقي جي سفارش سروس

Chia-Min Lin طرفان

ڪلائنٽ: LinStudio

انٽرفيس درجي ۾ A' Design Award چاندي انعام فاتح

57,305

تصديق ٿيل ايوارڊ فاتح

ڊزائين #91014 لاءِ ايوارڊ جي حيثيت جي تصديق ڪريو

A' Design Award تي ڏسو پريس رليز ايوارڊ بيج

رنگ

ڊزائن

Musiac موسيقي جي سفارش سروس بابت

ميوزڪ هڪ ميوزڪ رپوٽر انجڻ آهي ، ان جي استعمال ڪندڙن لاءِ صحيح آپشنز ڳولڻ لاءِ چالو سامعين ڪتب آڻيو. ان جو مقصد الگورٿم جي خودمختياري کي چئلينج ڪرڻ لاءِ متبادل انٽرنيٽ پيش ڪرڻ جي تجويز آهي. filterاڻ جي فلٽرنگ هڪ ناگزير ڳولا وارو طريقو بڻجي وئي آهي. تنهن هوندي ، اهو گونج چيمبر جا اثر پيدا ڪري ٿو ۽ صارفين کي پنهنجن آرام واري علائقي ۾ پابند ڪري انهن جي ترجيحن جي سخت طريقي سان عمل ڪندي. صارف غير فعال ٿي وڃن ٿا ۽ مشين کي اختيار ڪرڻ وارن سوالن کي روڪي ٿو. اختيارن جو جائزو وٺڻ لاءِ وقت گذارڻ شايد وڏي جيو قيمت وڌائي ، پر اها ڪوشش آهي جيڪا هڪ معنيٰ خيز تجربو پيدا ڪري.

سڃاڻپ

A' Design Award چاندي انعام بابت

Musiac موسيقي جي سفارش سروس A' Design Award چاندي انعام جو کٽيندڙ آهي، غير معمولي حد تائين معزز اعتراف جيڪو اعليٰ ترين درجي جي، شاندار ۽ متاثر ڪندڙ ڊيزائنن کي ڏنو وڃي ٿو. چاندي جا کٽيندڙ تمام گهڻا روشن ۽ انتهائي تخليقي ڪم آهن، جيڪي پنهنجي ڊيزائنرن جي شاندار مهارت، صلاحيت ۽ ذڪاوت کي ظاهر ڪن ٿا. Musiac موسيقي جي سفارش سروس کان اميد ڪئي وڃي ٿي ته اهو هڪ شاندار نمائشي ڪم هوندو، قابل ذڪر حد تائين پيشه ورانه ۽ ڳوڙهي جدت وارو. هي ڊيزائن جنهن A' Design Award چاندي انعام کٽيو آهي، هڪ انتهائي قابل ذڪر، ساراهيل ايجاد آهي جيڪا فائدو ڏئي ٿي ۽ فن، سائنس، ڊيزائن ۽ ٽيڪنالاجي جون حدون وڌائي ٿي. Musiac موسيقي جي سفارش سروس هڪ اهڙي تخليق آهي جنهن تي گهڻا حسد ڪن ٿا، جيڪا خواهشون جاڳائي ٿي، مثبت جذبا آڻي ٿي ۽ حيرت ۽ تعجب جي چڻنگ ٻاري ٿي.

ڊزائن جي پسمنظر

پريس ڪِٽ

ڊزائنر طرفان فراهم ڪيل ڪريڊٽس، ٽيم ميمبرز ۽ ٽيڪنيڪل تفصيلات.

منفرد خاصيتون: 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.
ماپ ۽ پيڪيجنگ: 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.
ٽيگ: Algorithm autocracy, Ethical interface, Information architecture, Data visualization, User empowerment, Collaborative filtering
پيداواري ٽيڪنالاجي: 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.
ڊزائين چيلينج: 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.
تحقيق: 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.
الهام: 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.
منصوبي جو عرصو: Musiac was a one year project done in 2018, Detroit.
عمل جو فلو: 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.
تصوير ڪريڊٽ: All materials required in this project doesn't need attributions. Licenses are attached in the documentation appendix.