Explainable movie recommendation systems by using story-based similarity

The goal of this paper is to provide a story-based explanation for movie recommendation systems, achieved by a multiaspect explanation and narrative analysis methods. We explain how and why particular movies are similar based on following two aspects: (i) composition of movie characters and (ii) interactions among the characters. These aspects correspond to story-based features of the movies that are extracted from character networks (i.e., social networks among the characters). By using the story-based features, we can explain the reason why two arbitrary movies are similar or not. We anticipate that the proposed method could improve the explainability of the recommender systems for movies.