E-MMKGR creates specialized knowledge graphs for e-commerce applications
Framework addresses limitations in current multimodal recommender systems
Experiments show significant improvements in recommendation recall and product search
Unified representations provide semantic foundation across diverse tasks
Research bridges Information Retrieval and Artificial Intelligence disciplines
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Researchers Jiwoo Kang and Yeon-Chang Lee introduced E-MMKGR, a novel framework for e-commerce applications that constructs a specialized Multimodal Knowledge Graph to enhance recommender systems, in a paper submitted to arXiv on February 24, 2026, addressing limitations in current multimodal systems that restrict modality extensibility and task generalization. The paper presents E-MMKGR as a solution to the constraints of existing multimodal recommender systems (MMRSs), which typically enhance collaborative filtering by leveraging item-side modalities but rely on fixed sets of modalities and task-specific objectives. The framework constructs an e-commerce-specific Multimodal Knowledge Graph (E-MMKG) and employs Graph Neural Network (GNN)-based propagation and knowledge graph-oriented optimization to learn unified item representations that serve as a shared semantic foundation applicable across diverse tasks. The researchers validated their approach through experiments using real-world Amazon datasets, demonstrating substantial improvements of up to 10.18% in Recall@10 for recommendation tasks and up to 21.72% over vector-based retrieval for product search functionality, highlighting the framework's effectiveness and extensibility for enhancing e-commerce platforms.
🏷️ Themes
Knowledge Graphs, E-commerce Technology, Multimodal Systems
Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs.
One prominent example is molecular drug design. Each input sample is a graph representation of a molecule, where atoms form the nodes and chemical bonds between atoms form the...
A recommender system, also called a recommendation algorithm, recommendation engine, or recommendation platform, is a type of information filtering system that suggests items most relevant to a particular user. The value of these systems becomes particularly evident in scenarios where users must sel...
Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be base...
# Artificial Intelligence (AI)
**Artificial Intelligence (AI)** is a specialized field of computer science dedicated to the development and study of computational systems capable of performing tasks typically associated with human intelligence. These tasks include learning, reasoning, problem-solvi...
--> Computer Science > Information Retrieval arXiv:2602.20877 [Submitted on 24 Feb 2026] Title: E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications Authors: Jiwoo Kang , Yeon-Chang Lee View a PDF of the paper titled E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications, by Jiwoo Kang and Yeon-Chang Lee View PDF HTML Abstract: Multimodal recommender systems enhance collaborative filtering by leveraging item-side modalities, but their reliance on a fixed set of modalities and task-specific objectives limits both modality extensibility and task generalization. We propose E-MMKGR, a framework that constructs an e-commerce-specific Multimodal Knowledge Graph E-MMKG and learns unified item representations through GNN-based propagation and KG-oriented optimization. These representations provide a shared semantic foundation applicable to diverse tasks. Experiments on real-world Amazon datasets show improvements of up to 10.18% in Recall@10 for recommendation and up to 21.72% over vector-based retrieval for product search, demonstrating the effectiveness and extensibility of our approach. Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20877 [cs.IR] (or arXiv:2602.20877v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2602.20877 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jiwoo Kang [ view email ] [v1] Tue, 24 Feb 2026 13:19:42 UTC (4,486 KB) Full-text links: Access Paper: View a PDF of the paper titled E-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications, by Jiwoo Kang and Yeon-Chang Lee View PDF HTML TeX Source view license Current browse context: cs.IR < prev | next > new | recent | 2026-02 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: ...
