What’s Next? A Recommendation System for Industrial Training

• Published in: Data science and engineering 2018-09, Vol.3 (3), p.232-247
• Main Authors: Srivastava, Rajiv, Palshikar, Girish Keshav, Chaurasia, Saheb, Dixit, Arati
• Format: Article
• Language: English
• Subjects: Algorithm Analysis and Problem Complexity; Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data mining; Data Mining and Knowledge Discovery; Database Management; Industrial training; Leadership; Personalized recommendation; Physics; Project management; Recommender systems; Sequence matching; Sequence mining; Statistics for Engineering; Systems and Data Security; Training
• ISSN: 2364-1185; 2364-1541
• DOI: 10.1007/s41019-018-0076-2

Continuous training is crucial for creating and maintaining the right skill-profile for the industrial organization’s workforce. There is a tremendous variety in the available trainings within an organization: technical, project management, quality, leadership, domain-specific, soft-skills, etc. Hence it is important to assist the employee in choosing the best trainings, which perfectly suits her background, project needs and career goals. In this paper, we focus on algorithms for training recommendation in an industrial setting. We formalize the problem of next training recommendation, taking into account the employee’s training and work history. We present several new unsupervised sequence mining algorithms to mine the past trainings data from the organization for arriving at personalized next training recommendation. Using the real-life data about trainings of 118,587 employees over 5019 distinct trainings from a large multi-national IT organization, we show that these algorithms outperform several standard recommendation engine algorithms as well as those based on standard sequence mining algorithms.