Analyzing User Requests for Anime Recommendations

Abstract

Anime is increasingly becoming recognized as an important commercial product and cultural artifact. However, little is known regarding users' information needs and behavior related to anime. This study specifically attempts to improve our understanding of how people seek anime recommendations. We analyzed 546 user questions in natural language, collected from a Korean Q&A website Naver Knowledge-iN, where users are asking for anime recommendations. The findings suggest the importance of establishing robust metadata for the seven commonly used features for anime recommenders (i.e., title, genre, artistic style, story, character description, series title, and mood) in digital libraries, as well as allowing users to specify known anime and series titles as examples for seeking similar items, or examples of the kinds of items to be excluded.