A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
2017
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a user will like a manga that received few ratings from the community? Content-based techniques can alleviate this issue but require extra information, that is usually expensive to gather. In this paper, we use a deep learning technique, Illustration2Vec, to easily extract tag information from the manga and anime posters
doi:10.1109/icdar.2017.287
dblp:conf/icdar/VieYLCCCK17
fatcat:ufxtqthwuvagzendujoi5mmhtm