Develop A Hybrid Recommendation System to Optimize Knowledge Utilization: A Case Study of an Indonesia Telecommunication Company

Abstract

With rapid industrial digitalization, companies need to strengthen core competences and digital talents within organizations to achieve competitive advantage. Companies have been developing digital knowledge management practices for acquisition, inventory, transfer, etc. But, to achieve a successful knowledge diffusion, increasing knowledge stock alone isn’t enough. As the knowledge stock increases, user choice for learning is also increasing. Reliable recommendation system is required as the selecting knowledge to learn in repository grows more complex. Ideal competences for each position need to be addressed as the company has done assessment for their ideal workforce. Meanwhile, every knowledge user has their own preferences which can be seen from registered preferences and learning history. Hybrid recommendation approaches from content based filtering and collaborative filtering can be utilized to address these issues. Finally, to complete this study of knowledge utilization optimization using machine learning, we will use knowledge stock, and human capital development data from Telkom Indonesia to develop a more personal and efficient knowledge platform. The proposed model can be used as a basis for further development of knowledge management platforms.

Keywords: Knowledge Management; Knowledge Utilization; Recommender System; Machine Learning;