Multi-View Graph Convolution Network for Internal Talent Recommendation Based on Enterprise Emails

TL;DR

This paper introduces a dual-graph convolutional network framework that models employee task similarity and interaction patterns from email data to improve internal talent recommendation with high interpretability.

Contribution

It presents a novel adaptive fusion model that combines semantic and structural employee data, outperforming existing methods and providing insights into different job family requirements.

Findings

Achieved 40.9% Hit@100 performance.

Demonstrated high interpretability in model decisions.

Outperformed baseline fusion strategies.

Abstract

Internal talent recommendation is a critical strategy for organizational continuity, yet conventional approaches suffer from structural limitations, often overlooking qualified candidates by relying on the narrow perspective of a few managers. To address this challenge, we propose a novel framework that models two distinct dimensions of an employee's position fit from email data: WHAT they do (semantic similarity of tasks) and HOW they work (structural characteristics of their interactions and collaborations). These dimensions are represented as independent graphs and adaptively fused using a Dual Graph Convolutional Network (GCN) with a gating mechanism. Experiments show that our proposed gating-based fusion model significantly outperforms other fusion strategies and a heuristic baseline, achieving a top performance of 40.9% on Hit@100. Importantly, it is worth noting that the model demonstrates high interpretability by learning distinct, context-aware fusion strategies for different job families. For example, it learned to prioritize relational (HOW) data for 'sales and marketing' job families while applying a balanced approach for 'research' job families. This research offers a quantitative and comprehensive framework for internal talent discovery, minimizing the risk of candidate omission inherent in traditional methods. Its primary contribution lies in its ability to empirically determine the optimal fusion ratio between task alignment (WHAT) and collaborative patterns (HOW), which is required for employees to succeed in the new positions, thereby offering important practical implications.