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

TL;DR

This work 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.

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.

Figures (13)

• Figure 1: An illustration of a panel-by-panel extraction of images from a narrative scroll painting and alignment with the text of an ancient doggerel poem written on handmade paper using black ink. Many parts of the original manuscript of the poem are missing or lost.
• Figure 2: The GeMi recommendation system. Fine-tuned Vision Language models are used to extract features, learn latent graph structures, and embeddings using graph convolutions. User-Panel preference matrices are used to augment recommendations.
• Figure 3: Distribution of the number of panels per scroll in the two phases of data collection from field surveys.
• Figure 4: A toy example from GeMi illustrating two types of item relations -- collaborative and semantic. Both can be used to learn the latent graph structures.
• Figure 5: Effect of the Top-$k$ recommendation cutoff on Precision@5 across Animal, Mythology, and Tree categories for GeMi variants under different feature backbones (LlamaSigCLIP and LlamaVAE). Results are reported as mean $\pm$ standard deviation over multiple runs.