Collaboration-Aware Hybrid Learning for Knowledge Development Prediction

Liyi Chen, Chuan Qin, Ying Sun, XIN SONG, Tong Xu, Hengshu Zhu, Hui Xiong

Keywords: Knowledge development, knowledge management platform, web mining, content analysis

Abstract:

In recent years, the rise of online knowledge management platforms has significantly improved work efficiency in enterprises.
Knowledge development prediction, as a critical application within these platforms, enables organizations to proactively address knowledge gaps and align their learning initiatives with evolving job requirements. However, it still confronts challenges in exploring collaborative networks and adapting to ecological situations in working environment. To this end, in this paper, we propose a Collaboration-Aware Hybrid Learning approach (CAHL) for predicting the future knowledge acquisition of employees and quantifying the impact of various knowledge learning patterns. Specifically, to fully harness the inherent rules of knowledge development, we first learn the knowledge co-occurrence and prerequisite relationships with an association prompt attention mechanism to generate effective knowledge representations through a specially-designed Job Knowledge Embedding module. Then, we aggregate the features of mastering knowledge and work collaborators for employee representations in another Employee Embedding module. Moreover, we propose to model the process of employee knowledge development via a Hybrid Learning Simulation module that integrates both collaborative learning and self learning to predict future-acquired job knowledge of employees. Finally, extensive experiments conducted on a real-world dataset clearly validate the effectiveness of CAHL.

Track: Web Mining and Content Analysis

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Submission Number: 96