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

TL;DR

This paper introduces BALSE, a novel collaborative filtering model that leverages deep learning-extracted poster tags to improve cold-start manga recommendations and interpret user preferences.

Contribution

It presents BALSE, a new model that combines content-based poster tags with collaborative filtering, enhancing recommendations for less-known manga.

Findings

Significant improvement in recommendation quality for cold-start manga.

Effective use of poster tags extracted by Illustration2Vec.

Provides interpretable insights into user preferences.

Abstract

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.