Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario

Jill-Jenn Vie, Florian Yger, Ryan Lahfa, Basile Clement, Kevin Cocchi, Thomas Chalumeau, Hisashi Kashima
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
more » ... e.g., sword, or ponytail). We propose BALSE (Blended Alternate Least Squares with Explanation), a new model for collaborative filtering, that benefits from this extra information to recommend mangas. We show, using real data from an online manga recommender system called Mangaki, that our model improves substantially the quality of recommendations, especially for less-known manga, and is able to provide an interpretation of the taste of the users.
doi:10.1109/icdar.2017.287 dblp:conf/icdar/VieYLCCCK17 fatcat:ufxtqthwuvagzendujoi5mmhtm