Essay About Recommender System And Telecommunication Industries

Recommender System for Telecommunication Industries – Zambia Telecoms
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RECOMMENDER SYSTEM FORTELECOMMUNICATION INDUSTRIES: ZAMBIA TELECOMSMulizwa Soft, Mr David Zulu, Ruzive MazhanduUniversity of Zambia, School of Natural Sciences , Computer Science department.         Abstract: Recommender systems use machine learning algorithms and artificial intelligence techniques to recommend products to customers, these algorithms use historical data of purchases of other people to determine which products to recommend to a particular customer, in general recommender systems are designed in such a way that they automatically generate personalized suggestions of products to customers. With the competitiveness that is growing in the Zambian telecom industry as a result of the new fourth mobile telecom service provider, telecommunication operators are looking for ways of attracting and keeping their subscribers on their network by giving affordable products to their subscribers, because they lack the ability to manage their customer retention rate, one of the main reason is that 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 seeking for cheap affordable products proves to be a severe problem for Telecom providers as they experience subscriber base and revenue shrinkage. This research paper describes a Recommender System for Telecommunication companies using call detail reports (CDR’s), machine learning algorithms and big data concepts.Keywords: Recommender systems, Telecom products/services, Machine learning algorithms, big data, Business Intelligence.INTRODUCTIONBackground information• With increasing number of mobile telecommunications operators in Zambia, a customer is entitled with unlimited freedom to switch from one mobile operator to another if he is not satisfied with the services or pricing their providers are providing. This trend is not good for operators as they lose their revenue because of customers switching from one provider to another [1]Statement of the problemTelecommunication 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 Telecom providers as they experience subscriber base and revenue shrinkage, increasing churn rate causes a loss of future incomes [2].Aim of the study•Implement         a recommender        system for Telecommunications CompanyObjectives    Study and examine the current existing recommender systems such as Netflix, Amazon, and EBay.Establish challenges telecommunication companies in Zambia face in terms of low revenue, churn, fraud etc. •        Determine how to process the raw call detail report file’s using the concept of big data.Design and implement a product recommender system which will recommend products that a subscriber is more likely to use. Research questions   How do we analyze the relationship between telecommunication subscribers and telecommunication products?What do the challenges telecommunication companies face that lead to high revenue loss, churn and bad customer experience?How do we process approximately five billion files/day?How best will Product recommender systems for mobile technology be utilized in order to assist solving the problem of low revenue, churn and fraud?Significance of the study  Recommender based systems implemented using the concept of big data, machine learning or deep learning algorithms have a lot of advantages which will benefit telecommunication companies, for example better user experience, increased Traffic/