A Two-Layer Deep Learning Approach for R&D Partner Recommendation in the Self-Driving Vehicle Industry

Juite Wang, Ying-Pei Kao

2025

Abstract

This study presents a two-stage deep learning framework for recommending strategic R&D partners in the self-driving vehicle (SDV) industry. Leveraging 165,775 U.S. patent applications from 2015 to 2023, we constructed a co-patent network and extracted node, edge, and topological features to represent organizational attributes, collaboration intensity, and network structure. These features were integrated using a hybrid Graph Neural Network (GNN) and Deep Neural Network (DNN) architecture to predict future collaborations. The model achieved high predictive performance (accuracy = 96.65%, precision = 70.83%, recall = 66.92%, F1 = 68.82%, and AUPRC = 78.93%) and demonstrated its ability to identify both established and emerging partners. Community detection revealed influential clusters anchored by firms like Toyota and Hyundai. Case analyses showed that the model can recommend both historical and emerging R&D partners. Compared to prior work, this study contributes a scalable, data-driven approach that incorporates deep structural and semantic signals to improve partner selection accuracy. The framework advances patent analytics by linking network-based learning with partner recommendations, offering practical implications for R&D planning in complex technology-based industries.

Paper Citation

in Harvard Style

Wang J. and Kao Y. (2025). A Two-Layer Deep Learning Approach for R&D Partner Recommendation in the Self-Driving Vehicle Industry. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 60-66. DOI: 10.5220/0013683100004000

in Bibtex Style

@conference{kdir25,
author={Juite Wang and Ying-Pei Kao},
title={A Two-Layer Deep Learning Approach for R&D Partner Recommendation in the Self-Driving Vehicle Industry},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={60-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013683100004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - A Two-Layer Deep Learning Approach for R&D Partner Recommendation in the Self-Driving Vehicle Industry
SN -
AU - Wang J.
AU - Kao Y.
PY - 2025
SP - 60
EP - 66
DO - 10.5220/0013683100004000
PB - SciTePress