Artwork Recommendations based on User Preferences: Integrating Clustering Analysis with Visual Features

Abstract

Recently, recommendation systems have become one of the important elements for sales and marketing, and their application is almost essential in the shopping and cultural industries. Despite the increase in online exhibitions and the growing audience engaging with artworks in digital spaces, the utilization of artwork recommendation systems remains inadequate. Thus, this study proposes an artwork recommendation system, which provides artwork groups based on a visual clustering technique and user preferences with WikiArt datasets. The visual attributes of artworks were extracted using VGG16, and K-means clustering was utilized to group a set of images according to their feature similarities. To generate recommendations, new artworks were randomly selected from particular clusters, taking into account users’ preferences. Then, an experiment was conducted to investigate whether the recommended artworks satisfied the users. The statistical results indicate that users’ perceived satisfaction with the recommended artworks is notably more positive compared to their satisfaction with traditional suggested artworks. Based on this study’s findings, we present implications and limitations for future research.