Mangaki will be at the Anime & Manga Symposium @ Anime Expo, Los Angeles!18 May 2017
Hey ! Je vais me présenter au Anime & Manga Studies Symposium @AnimeExpo du 1er au 4 juillet, à Los Angeles ! ✈️🇺🇸— Mangaki (@MangakiFR) 25 mai 2017
Car oui, nous avons été acceptés pour faire une présentation à la conférence académique d’Anime Expo.
Everyone regularly ask themselves what movie, series or book they should watch next, according to their taste. Mangaki wants to innovate access to Japanese culture by providing a unique user experience through a recommender system.
When a user shows up, Mangaki asks them to rate a few works. Based on their answers, they receive tailored anime recommendations. Mangaki’s machine learning techniques attempt to “guess” the taste of new users, by geometrically positioning their ratings within those collected from other users. Indeed, we will show that a simple factor analysis on the anonymized data (325,000 ratings from 2,220 users and 15,000 works) can reveal interesting and counterintuitive categories of manga that are liked together.
Mangaki started as a French student project in 2014 (it is so much fun to conduct research when it is unleashed on real data!). It received a prize from Microsoft (2015) and the Japanese Cultural Institute in Paris (2016): we won a trip to Tokyo to meet Japanese companies and investors, who were surprised to learn that we wanted to stay non-profit. The founder now holds a PhD in CS and works as a researcher in RIKEN, Tokyo.
In its mission to promote transparency and education, all the code of the Mangaki platform is open source. Consequently, it becomes possible for anyone to understand better the algorithms behind recommender systems. We regularly hold conferences for students, from high school to master’s degree.
The Mangaki dataset will also be released for academic purposes (humanities: dōzo!), while respecting the users’ privacy. We are currently organizing a data challenge with Kashima’s Machine Learning Lab in Kyoto University, where students will have to improve the accuracy of the recommendations. There is plenty of exciting research yet to be made, notably the automatic detection of NSFW posters using deep learning.