In recent years, businesses are increasingly realizing the importance of knowing their customers better. E-businesses, as well as brick-and-mortar businesses, are focusing their marketing efforts on building lasting ties with customers through improved Customer Relationship Management (CRM). Under these circumstances, this study investigates two CRM related issues:
Recommendation system in Part Ⅱand CRM success factor model in Part Ⅲ
In Part Ⅱ, we focus on improving personalized recommendation of which collaborative filtering algorithm combines with intelligent data mining techniques.
Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously.
This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM clusterindexing CBR with validation against control algorithms through an open dataset
of user preference.
The performance of our model yields superior results compared to memorybased CF techniques and other previous hybrid CF models. Our model yields also superior performance when compared with other traditional memory-based CF algorithms and other NN (Neural Network) and induction based CF prediction algor...