RecKG-Web: A web-based interactive visualization tool for standardized knowledge graphs in recommender systems

Knowledge graphs (KG) enhance recommender systems (RS) by addressing challenges like the long-tail and cold-start problems through rich semantic relationships. However, inconsistent metadata across RS datasets limits interoperability. To solve this, we present RecKG-Web, a user-friendly application that standardizes and integrates heterogeneous datasets using the RecKG format. It supports data upload, mapping, graph-based visualization, and dataset export, enabling cross-domain recommendations and improving accessibility for researchers and practitioners. RecKG-Web bridges the gap between KG theory and practice, advancing KG-aware RS development.

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Publication Details

Journal
SoftwareX
Published
2025-08-28
DOI
https://doi.org/10.1016/j.softx.2025.102319
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

RecKG-Web: A web-based interactive visualization tool for standardized knowledge graphs in recommender systems

Myung Seok Jeon, Euijong Lee, Young-Duk Seo, Junhyung Park
SoftwareX
Recommender Systems and Techniques
article

RecKG-Web: A web-based interactive visualization tool for standardized knowledge graphs in recommender systems

Myung Seok Jeon, Euijong Lee, Young-Duk Seo, Junhyung Park
article en

Abstract

Knowledge graphs (KG) enhance recommender systems (RS) by addressing challenges like the long-tail and cold-start problems through rich semantic relationships. However, inconsistent metadata across RS datasets limits interoperability. To solve this, we present RecKG-Web, a user-friendly application that standardizes and integrates heterogeneous datasets using the RecKG format. It supports data upload, mapping, graph-based visualization, and dataset export, enabling cross-domain recommendations and improving accessibility for researchers and practitioners. RecKG-Web bridges the gap between KG theory and practice, advancing KG-aware RS development.

SoftwareXVol. 31
Chungbuk National University (KR), Inha University (KR)
Inha University, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion
Openalex Percentile: Top 66%
Recommender Systems and Techniques
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RecKG-Web: A web-based interactive visualization tool for standardized knowledge graphs in recommender systems — Myung Seok Jeon, Euijong Lee, et al. · SoftwareX (2025) | TGRS Research Map | TGRS