Soban Arshad¹, Khalid Iqbal^1,*, Sheneela Naz², Sadaf Yasmin¹, Zobia Rehman²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4283-4301, 2022, DOI:10.32604/cmc.2022.025442

Abstract Telecom industry relies on churn prediction models to retain their customers. These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers. Retention not only contributes to the profit of an organization, but it is also important for upholding a position in the competitive market. In the past, numerous churn prediction models have been proposed, but the current models have a number of flaws that prevent them from being used in real-world large-scale telecom datasets. These schemes, fail to incorporate frequently changing requirements. Data sparsity, noisy data, and the… More >

Adel Binbusayyis^*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 501-513, 2022, DOI:10.32604/iasc.2022.022239

Abstract The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from two inherent issues namely, rating… More >

Thavavel Vaiyapuri^*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 487-503, 2021, DOI:10.32604/cmc.2021.015998

Abstract The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques—including collaborative filtering (CF), which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems, preventing… More >