scholarly journals Toward Mobile Telecommunication Recommendation System through Intelligent Customers Categorization

2014 ◽  
Vol 12 (7) ◽  
pp. 3651-3658
Author(s):  
Rawan Ghnemat ◽  
Edward Jaser
Keyword(s):  
Recommendation System ◽  
Retention Rate ◽  
Fuzzy Rule ◽  
Personalized Recommendation ◽  
Mobile Telecommunication ◽  
Customer Data ◽  
Superior Result ◽  
Business Plans ◽  
Telecommunication Companies ◽  
Competitive Prices

Now a day, usage of mobile devices is becoming indispensable. This is evident with current mobile penetration rates reaching 100% and even more in some countries. Customers across the world are enjoying competitive prices due to high competition among telecommunication companies. As a result of this, it is mandatory for mobile companies to provide high quality services to their customers to retain them. One aspect which will maximize customers’ trust and lead to high retention rate is to offer them a suitable plan that matches their usage.  Mobile customer usage categorization is therefore an essential task to develop intelligent business plans. Personalized recommendation system is needed to dynamically adapt the different customer behaviours with the most appropriate plan for them. In this paper we propose a new automatic approach for costumers’ categorization. This will be the basis for the recommendation system. The proposed method is built using Fuzzy rule and aims at usage behaviour prediction. The rules was extracted from real customer data obtained from a leading provider. Comparison study with other categorization methods has been conducted and showed superior result and demonstrated the potential advantage of the proposed fuzzy based method.

scholarly journals Evaluation of Recommendation System for Sustainable E-Commerce: Accuracy, Diversity and Customer Satisfaction

2020 ◽  
Author(s):  
Qinglong Li ◽  
Ilyoung Choi ◽  
Jaekyeong Kim
Keyword(s):  
Customer Satisfaction ◽  
Deep Neural Networks ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Sustainable Competitive Advantage ◽  
Customer Data ◽  
The Sustainable Development ◽  
Personalized Services ◽  
Recommendation Algorithms ◽  
Positive Effect

With the development of information technology and the popularization of mobile devices, collecting various types of customer data such as purchase history or behavior patterns became possible. As the customer data being accumulated, there is a growing demand for personalized recommendation services that provide customized services to customers. Currently, global e-commerce companies offer personalized recommendation services to gain a sustainable competitive advantage. However, previous research on recommendation systems has consistently raised the issue that the accuracy of recommendation algorithms does not necessarily lead to the satisfaction of recommended service users. It also claims that customers are highly satisfied when the recommendation system recommends diverse items to them. In this study, we want to identify the factors that determine customer satisfaction when using the recommendation system which provides personalized services. To this end, we developed a recommendation system based on Deep Neural Networks (DNN) and measured the accuracy of recommendation service, the diversity of recommended items and customer satisfaction with the recommendation service. The experimental results of is the study showed that both recommendation system accuracy and diversity would have a positive effect on customer satisfaction. These results can further improve customer satisfaction with the recommendation system and promote the sustainable development of e-commerce.

A Personalized Recommendation Model in E Commerce Based on TOPSIS Algorithm

2014 ◽  
Vol 12 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Liang Wang ◽  
Runtong Zhang ◽  
Huan Ruan
Keyword(s):  
Recommendation System ◽  
Personalized Recommendation ◽  
User Evaluation ◽  
Evaluation Indexes ◽  
Structured Products ◽  
Digital Books ◽  
Improved Model

From the perspective of performance and universality, this paper analyzed the characteristics of typical technologies for personalized recommendation system, and then made a basic architecture for the improved model. With the architecture, this paper introduced a personalized recommendation model in e-commerce system. The model is based on an n-tiers structure and the TOPSIS algorithm, first standardize the user evaluation indexes, and then determine the indexes weights according to user's needs, and finally calculate the personalized recommendation results. This model can be applied to a variety of e-commerce applications, especially for the e-commerce application with structured or semi-structured products such as digital books, journals and other publications.

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