Product recommender system for telecommunication industries: a case of Zambia telecommunications companies

Recommender systems have become increasingly popular in recent years, and are utilized in a variety of areas including movies, music, news, products, research articles, search queries, social tags, and products in general they are designed to automatically generate personalized suggestions of products/services to customers. With the competitiveness that is growing in the telecommunication industry, telecommunication operators seek ways to attract and keep the subscribers on their network, Its notable that telecommunication operators lack the ability to manage their customer retention rate because they do not have a personalized way of recommending products and services to their subscribers, as a result subscribers tend to migrate to new providers. This trend of subscribers migrating to new providers proves to be a severe problem for Telecommunication providers as they experience subscriber base and revenue shrinkage. This dissertation describes a Recommender System for Telecommunication companies using call detail reports (CDR’s), machine learning algorithms and big data concepts. Experimental results demonstrate the effectiveness of the proposed approach and the initial application shows that recommender systems can effectively help customers to select the most suitable mobile products or services.