System for Recommending Telecommunication Packages Based on the Deep and Cross Network. (19th April 2022)

• Record Type: Journal Article
• Authors: Shi, Congming; Wang, Wen; Wei, Shoulin; Lv, Feiya
• Other Names: Hashmi Mohammad Farukh Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-04-19
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/2100841
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : With the evolution of the 5 th generation mobile network (5G), the telecommunications industry has considerably affected livelihoods and resulted in the development of national economies worldwide. To increase revenue per customer and secure long-term contracts with users, telecommunications firms and enterprises have launched diverse types of telecommunication packages to satisfy varied user requirements. Several systems for recommending telecommunication packages have been recently proposed. However, extracting effective feature information from large and complex consumption data remains challenging. Conventional methods for the recommendation of telecommunications packages either rely on complex expert feature engineering or fail to perform end-to-end deep learning (DL) during training. In this study, we propose a recommender system based on the Deep and Cross Network (DCN), deep belief network (DBN), embedding, and Word2Vec using the learning abilities of DL-based approaches. The proposed system fits the recommender system for telecommunication packages in terms of click-through rate prediction to provide a potential solution to the recommendation challenges faced by telecommunication enterprises. The proposed model captures the finite order interactional and deep hidden features. Additionally, the text information in the data is used to improve the model's recommendation capability. The proposed method also does not require feature engineering. We conducted comprehensive experiments using real-world datasets, the results of which demonstrated that our proposed method outperformed other methods based on DBNs, DCNs, deep factorization machines, and deep neural networks in terms of the area under the ROC curve, cross entropy (log loss), and recall metrics.