The Japanese Society for Artificial Intelligence

It is difficult for users to find paintings that match their tastes among the huge number of paintings available in museums and on the Internet. A painting recommendation system is a useful solution to this problem. However, it is difficult to construct a recommendation system based on user history, which is used for movies and music, because the unit price and the market size of paintings themselves are different. In order to construct a recommendation system that uses only the image features of the painting itself, this study explores the extraction of image features by machine learning. As features, we used features obtained from general-purpose image classification models and object detection models. The relationship between these features and the user's subjective preference for paintings was investigated by subject experiments.