||Services have been important role to perform economic activities. In order to fulfill customer needs and increase customer retention and loyalty, enterprises attempt to design high quality services by considering good features. With the development of Internet, service information overloading becomes an essential issue nowadays. There are existing recommendation systems to help customers to find suitable information. However, these systems are built based on the product perspective rather considering values of services. Hence, this study aims to develop a service recommendation system to increase quality of services.
The service recommendation system is to find out the list of key customers and the list of key service items by applying the RFM and association rules approaches. Accordingly, this study conducts three experiments to verify the feasibility of the service recommendation system and evaluate the suitability of recommended services. Meanwhile, this study also uses Precision, Recall and Fallout to assess the performance of the service recommendation system by comparing to the mechanism of Apriori algorithm. The results show that the service recommendation system has higher performance than the mechanism of Apriori algorithm.
The research contributions are addressed as follows. Service values can be numerically represented and preserved via the RFM analysis. Valuable services are first to serve customers. The service recommendation system can be applied into different fields by defining suitable services in order to decrease searching cost. Finally, researchers can extend our system by considering other factors in different industries and defining proper weights to provide customers with high quality services for the further research.