GeMi: A Graph-based, Multimodal Recommendation System for Narrative Scroll Paintings
Haimonti Dutta, Pruthvi Moluguri, Jin Dai, Saurabh Amarnath Mahindre

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…
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Taxonomy
TopicsAesthetic Perception and Analysis · Generative Adversarial Networks and Image Synthesis · Art History and Market Analysis
