What’s Next? A Recommendation System for Industrial Training

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. Hen...

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Bibliographic Details
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
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Summary: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.
ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-018-0076-2