Using Data Mining to Improve Decision-Making: Case Study of A Recommendation System Development

Abstract

Background and purpose This study aims to provide a practical perspective on how data mining techniques are used in the home appliance after-sales services. Study investigates on how can a recommendation system help a customer service company that plans to use data mining to improve decision making during its digital transformation process. In addition, study provides a detailed outline on the process for developing and analyzing platforms to improve data analytics for such companies. Methodology Case study approach is used for evaluating the usability of recommendation systems based on data mining approach in the context of home appliance after-sales services. We selected the latest platforms based on their relevance to the recommender system and their applicability to the functionality of the data mining system as trends in the system design. Results Evaluation of the impact on decision making shows how the application of data mining techniques in organizations can increase efficiency. Evaluation of the time taken to resolve the complaint, as a key attribute of service quality that affects customer satisfaction, and the positive results achieved by the recommendation system are presented. Conclusion This paper increases the understanding of the benefits of the data mining approach in the context of recommender systems. The benefits of data mining, an important component of advanced analytics, lead to an increase in business productivity through predictive analytics. For future research, other attributes or factors useful for the recommender systems can be considered to improve the quality of the results.

Deep Filter Context Network for Click-Through Rate Prediction

“…In an era of diversified market economies, the Internet economy accounts for an increasing share of the overall economy [38], and the rise and continuous development of e-commerce promote the progress of recommendation systems. As a data mining model, recommendation systems can analyze data in detail to help e-commerce companies to improve their decision-making, increase operational efficiency, and provide a better service to their customers [39]. In this paper, the DFCN model proposed in the context of e-commerce display advertising filters the huge volume of users' historical behavioral feature data, effectively suppressing the interference of non-relevant user history features with the relevant features and helping the attention mechanism to assign weights to each element more precisely and effectively.…”