A Music Recommender System for m-CRM: Collaborative Filtering using Web Mining and Ordinal Scale
Seok-kee Lee · Journal of the Korea Society of Computer and Information · 2008
As mobile Web technology becomes more increasingly applicable. the mobile contents market. especially the music downloading for mobile phones, has recorded remarkable growth. In spite of this rapid growth, customers experience high levels of frustration in the process of searching for desired music contents. It affects to a re-purchasing rate of customers and also. music mubile content providers experience a decrease in the benefit. Therefore, in aspects of a customer relationship management (CRM), a new way to increase a benefit by providing a convenient shopping environment to mobile customers is necessary. As an solution for this situation, we propose a new music recommender system to enhance the customers' search efficiency by combining collaborative filtering with mobile web mining and ordinal scale based customer preferences. Some experiments are also performed to verify that our proposed system is more effective than the current recommender systems in the mobile Web.