Titlebook: Recommender System for Improving Customer Loyalty; Katarzyna Tarnowska,Zbigniew W. Ras,Lynn Daniel Book 2020 Springer Nature Switzerland A

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Conclusions, a numerical decision table from the text, based on detecting the numerical values of sentiment polarity towards certain aspects of the service. This chapter concludes work done within this project and well as depicts plans for the future developments.

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Introduction,allenging to implement due to greater information flow and a much larger pool of creative talent throughout the world. However, a company that provides easy, reliable customer service is a tough competitor. A company with great customer experience has a very defensible strategy that is difficult to

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Customer Loyalty Improvement, Promoter Score (NPS). It presents the main decision problem questions in this domain that should be supported by the system and main motivation for building such system. It also describes the source dataset collected on the customer feedback with NPS as a decision attribute.

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Visual Data Analysis, System. The implemented web-based visual system supports a feature analysis of 38 client companies for each year between 2011–2016 in the area of customer service (divided into shop service/field service) and parts. It serves as a visualization for the feature selection method showing the relative

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Recommender System Based on Unstructured Data,ng customers. The idea is to limit the number of score benchmark questions, and let customers express their opinions in a free format. As a result the collected data will mainly contain open-ended text comments. This chapter presents a strategy to modify the existing recommender system built based o