Value-Added Services at a Mobile Telecommunication company provide customers with a variety of services. Value-added services generate significant revenue annually for telecommunication companies. Providing solutions that can provide customers of a telecommunication company with relevant and engaging services has become a major challenge in this field. Numerous methods have been proposed so far to analyze customer basket and provide related services. Although these methods have many applications, they still face difficulties in improving the accuracy of bids. This paper combines the X-Means algorithm, the ensemble learning system, and the N-List structure to analyze the customer portfolio of a mobile telecommunication company and provide value-added services. The X-Means algorithm is used to determine the optimal number of clusters and clustering of customers in a mobile telecommunication company. The ensemble learning algorithm is also used to assign categories to new Elder customers, and finally to the N-List structure for customer basket analysis. By simulating the proposed method and comparing it with other methods including KNN, SVM, and deep neural networks, the accuracy improved to about 7%.
Over the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.
The proposed method in this paper consists of three steps: initial clustering of all users and assigning new user to appropriate clusters, assigning appropriate weights to users' characteristics, and identifying new user’s adjacent users using hybrid similarity criteria and adjacency matrix of adjacent users’ rating to the movie services and calculating new user’s rating to each movie considering adjacent users’ rating and the similarity level of each adjacent user to the new user. The results show that the mean squared error of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. Also, MAE of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and RMSE of the proposed method decreased by 6.05% compared to SVD and approximately 6.02% compared to ApproSVD.
Value Added Services at Mobile Communications Company provide customers with a variety of services. Value added services generate significant revenue annually for telecommunications companies. Providing solutions that can provide customers of a communications company with relevant and engaging services has become a major challenge in this field. Numerous methods have been proposed so far to analyze customers' carts and provide related services. Despite the many applications that these methods have, they still face difficulties in improving the accuracy of bids. This paper combines the X-Means algorithm, the ensemble learning system, and the N-List structure to analyze the customer portfolio of a mobile communications company and provide value-added services. The X-Means algorithm is used to determine the optimal number of clusters and clustering of customers in a mobile communications company. The ensemble learning algorithm is also used to assign categories to new Elder customers, and finally to the N-List structure for customer basket analysis. By simulating the proposed method and comparing it with other methods including KNN, SVM, and deep neural networks, it has improved the accuracy of about 7%.
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