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

• URL: http://arxiv.org/abs/2603.00854v1
• Date: Sun, 01 Mar 2026 00:54:26 GMT
• Title: GeMi: A Graph-based, Multimodal Recommendation System for Narrative Scroll Paintings
• Authors: Haimonti Dutta, Pruthvi Moluguri, Jin Dai, Saurabh Amarnath Mahindre,
• Abstract summary: Graph Neural Network (GNN)-based recommendation systems are a special class of recommendation systems.<n>In this work, we present the design of a GNN-based recommendation system on a novel data set collected from field research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Recommender System for NFT Collectibles with Item Feature [24.201581738408045]
  This paper presents a recommender system for NFTs that utilize a variety of data sources to generate precise recommendations. We develop a data-efficient graph-based recommender system to efficiently capture the complex relationship between each item and users.
  arXiv  Detail & Related papers  (2024-03-27T06:59:39Z)
• Knowledge-Enhanced Recommendation with User-Centric Subgraph Network [38.814514460928386]
  We propose Knowledge-enhanced User-Centric subgraph Network (KUCNet) for effective recommendation. KUCNet is a subgraph learning approach with graph neural network (GNN) for effective recommendation. Our proposed method achieves accurate, efficient, and interpretable recommendations especially for new items.
  arXiv  Detail & Related papers  (2024-03-21T13:09:23Z)
• Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems [5.465420718331109]
  Graph-based collaborative filtering methods have prevailing performance for recommender systems. We propose an efficient generative model that jointly considers the preferences of users, the concurrence of items and some important graph structure information.
  arXiv  Detail & Related papers  (2023-11-30T11:49:33Z)
• Embedding in Recommender Systems: A Survey [54.55152033023537]
  This survey presents a comprehensive analysis of advances in recommender system embedding techniques.<n>In matrix-based scenarios, collaborative filtering generates embeddings that effectively model user-item preferences.<n>We introduce emerging approaches, including AutoML, hashing techniques, and quantization methods, to enhance performance.
  arXiv  Detail & Related papers  (2023-10-28T06:31:06Z)
• Impression-Aware Recommender Systems [53.48892326556546]
  We present a systematic literature review on recommender systems using impressions.<n>We define a theoretical framework to delimit recommender systems using impressions and a novel paradigm for personalized recommendations, called impression-aware recommender systems.
  arXiv  Detail & Related papers  (2023-08-15T16:16:02Z)
• Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation [57.67616822888859]
  We propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL) TMER-RL utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.
  arXiv  Detail & Related papers  (2021-11-24T04:34:26Z)
• Rating and aspect-based opinion graph embeddings for explainable recommendations [69.9674326582747]
  We propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews. We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders.
  arXiv  Detail & Related papers  (2021-07-07T14:07:07Z)
• Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations [66.83527496838937]
  We propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews. We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains. Our approach has the advantage of providing explanations which leverage aspect-based opinions given by users about recommended items.
  arXiv  Detail & Related papers  (2021-07-07T13:57:28Z)
• A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation [70.69134448863483]
  Research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models.
  arXiv  Detail & Related papers  (2021-04-27T08:03:52Z)
• A Survey on Knowledge Graph-Based Recommender Systems [65.50486149662564]
  We conduct a systematical survey of knowledge graph-based recommender systems. We focus on how the papers utilize the knowledge graph for accurate and explainable recommendation. We introduce datasets used in these works.
  arXiv  Detail & Related papers  (2020-02-28T02:26:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.