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.

翻译：推荐系统能有效管理当今日益增长的多模态数据，帮助用户发现感兴趣的新项目。这些系统可处理图像、文本、音频和视频等多种媒体类型，使得基于内容的推荐成为可能——既能利用从项目中提取的特征，又能结合用户偏好。基于图神经网络（GNN）的推荐系统是一类特殊的推荐系统，能够处理项目与用户之间的关系，因此对基于内容的推荐具有特殊吸引力。其流行还源于这类系统采用先进的机器学习技术（如图结构数据的深度学习）来挖掘用户与项目的交互关系。图中的节点可访问高阶邻居信息，并结合最先进的视觉-语言模型处理多模态内容；同时存在精心设计的嵌入、消息传递与传播算法。本研究基于实地调研收集的新型数据集，提出了一种基于GNN的推荐系统设计方案。该系统专为濒危表演艺术形式设计，利用多模态内容（文本与图像数据）为用户推荐可供观赏和购买的相似画作。据我们所知，目前尚无专门针对叙事卷轴画的推荐系统——因此本研究实现了多重目标：既助力艺术保护，构建濒危艺术藏品的数据存储系统，又开发出融合数据新颖特性与叙事卷轴画爱好者偏好的前沿推荐系统。