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Improving Recommendation Quality by Using Data - Including Machine Learning

Persol Career Case Study


Persol Career provides workers with diverse growth opportunities and offers a broad range of services contributing to the growth of organizations, aiming to realize an “infrastructure that generates growth for people and organizations.”

  • Enabled new recommendations based on machine learning results
  • Improved employees’ awareness of data use
  • Potential to build new business models with use of unstructured data


Conventional human resource services have offered recommendations based on several conditions provided by candidates and their experience. To further improve the quality of recommendations, there was a need to incorporate AI analytics.


Persol Career selected HDP as its core solution, along with using Spark for data processing and machine learning.


This new data architecture has enabled new recommendations based on machine learning, resulting in improved employee awareness of data use and better decision making.

Takaaki Saito, Manager, Persol Career

All employees, from the management team to those in the field, have growing hopes for data use. They are very positive about using data, thinking what results and services can be attained with certain data. The ultimate goal of data use is to transform business. Building an AI infrastructure is the first step towards changing the quality across the board.