Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Vie, Jill-Jênn; Yger, Florian; Lahfa, Ryan; Clement, Basile; Cocchi, Kévin; Chalumeau, Thomas; Kashima, Hisashi (2017), Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario, in Ogier, Jean-Marc; Garain, Utpal; Antonacopoulos, Apostolos, 14th IAPR International Conference on Document Analysis and Recognition (ICDAR 2017), 2nd International Workshop on coMics ANalysis, Processing and Understanding (MANPU 2017), IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ. 10.1109/ICDAR.2017.287
TypeCommunication / Conférence
Conference titleMANPU 2017 held in conjunction with ICDAR 2017
Book title14th IAPR International Conference on Document Analysis and Recognition (ICDAR 2017), 2nd International Workshop on coMics ANalysis, Processing and Understanding (MANPU 2017)
Book authorOgier, Jean-Marc; Garain, Utpal; Antonacopoulos, Apostolos
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Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)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 (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.
Subjects / Keywordscollaborative filtering; humanities; learning (artificial intelligence); least squares approximations; recommender systems
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