CDR Based Recommender System for Mobile Package Service Users
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Date
2023-09
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Addis Ababa University
Abstract
Due to increased competition, telecom operators are continually introducing new products and services. To make their services easy to use, to meet customer requirements, and to satisfy their customers' needs in terms of payment, operators have launched various telecommunication packages. Telecom operators have so many packages that customers are unaware of; some packages may go unnoticed even if they are useful. To overcome such problems, we need a recommender system that directly notifies customers based on their interests. Most research has been conducted on recommendation systems for web service users based on user ratings. In this paper, we propose a mobile package service recommendation system for customers.
The proposed recommendation system has two phases. The first is creating a relationship between customer usage and mobile packages by grouping customers based on their usage. To create a relationship between customers' usage and mobile packages, we have used the k-means clustering algorithm. The elbow method is used to determine the number of clusters for each service. The second phase is building a classification model that will recommend mobile packages for users. Two-month CDR data was used to build a classification model by using random forest (RF) and K-nearest neighbor (KNN) classifier algorithms.
The evaluation result shows KNN outperformed RF for weekly and monthly data usage plans with F1 scores of 90.4% and 96%, respectively, whereas RF outperformed for daily plans with an F1 score of 86.9%. On the other hand, RF outperformed KNN with F1 scores of 95.10% and 99.60% for daily and monthly voice usage plans, respectively. Similarly, KNN showcased better performance than RF on the weekly voice usage plan with an F1 score of 94.30%.
Generally, the strengths of each algorithm differ for different usage scenarios within the voice and data service domains.
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Keywords
Recommender system, Collaborative filtering, Content-based recommender systems, Mobile recommender systems, Clustering and Classification