Recommender Systems for Business, Healthcare and Information Security
The talk will present Intelligent Systems that support human decision-making processes (Decision Support Systems and Recommender Systems) based on big data analysis techniques (Data Mining) and algorithms in Machine Learning. The talk will focus on the real-world applications of such systems in the areas of business, healthcare, and information security.
Recommender Systems are new generation of Intelligent Systems that will increase the application areas in the future beyond its current e-commerce usage. In the presented research, the results of exploring the possibilities of applying Recommender Systems in the healthcare, business and information security will be discussed. The proposed and implemented prototype of a Recommender System for Diagnosis and Treatment of Tinnitus (RECTIN) will be presented. This knowledge-based system was built on historical patients and their visit data provided by a world-expert in treating a hearing disorder - tinnitus, Dr. Pawel Jastreboff. The recent doctoral research work concentrates on building a Recommender System in the area of business, supporting B2B clients in improving satisfaction for their customers and their company’s growth potential. The Customer Loyalty Improvement Recommender System (CLIRS) uses machine learning, big data, and text mining techniques (Natural Language Processing and Sentiment Analysis algorithms) as well as visualization techniques to provide the final recommendations to its business end users.
Katarzyna Tarnowska received her MS in Computer Science from Technische Universitat Berlin in Germany and Warsaw University of Technology in Poland. Currently, she is a Ph.D. candidate at University of North Carolina at Charlotte and works as a Research and Teaching Assistant in the Knowledge Discovery in Databases Lab. Her main research focus is Data Mining, Natural Language Processing and Recommender Systems. Current and past projects include the development of a system recommending strategy to improve customer satisfaction of a company and a system in the area of health informatics supporting treatment and diagnosis of tinnitus. Her teaching interests include database systems and data analytics/big data.