GeMi: A Graph-based, Multimodal Recommendation System for Narrative Scroll Paintings

TL;DR

This paper introduces GeMi, a graph neural network-based recommendation system tailored for narrative scroll paintings, utilizing multimodal data and novel graph algorithms to support art conservation and user engagement.

Contribution

It presents the first recommendation system specifically designed for narrative scroll paintings, combining multimodal content analysis with graph neural networks on a new dataset.

Findings

Effective recommendation of similar paintings based on multimodal features.

Supports art conservation and data storage for endangered art forms.

Leverages advanced GNN techniques for improved content-based recommendations.

Abstract

Recommendation Systems are effective in managing the ever-increasing amount of multimodal data available today and help users discover interesting new items. These systems can handle various media types such as images, text, audio, and video data, and this has made it possible to handle content-based recommendation utilizing features extracted from items while also incorporating user preferences. Graph Neural Network (GNN)-based recommendation systems are a special class of recommendation systems that can handle relationships between items and users, making them particularly attractive for content-based recommendations. Their popularity also stems from the fact that they use advanced machine learning techniques, such as deep learning on graph-structured data, to exploit user-to-item interactions. The nodes in the graph can access higher-order neighbor information along with state-of-the-art vision-language models for processing multimodal content, and there are well-designed algorithms for embedding, message passing, and propagation. In this work, we present the design of a GNN-based recommendation system on a novel data set collected from field research. Designed for an endangered performing art form, the recommendation system uses multimodal content (text and image data) to suggest similar paintings for viewing and purchase. To the best of our knowledge, there is no recommendation system designed for narrative scroll paintings -- our work therefore serves several purposes, including art conservation, a data storage system for endangered art objects, and a state-of-the-art recommendation system that leverages both the novel characteristics of the data and preferences of the user population interested in narrative scroll paintings.