Applied Fuzzy and Analytic Hierarchy Process in Hybrid Recommendation Approaches for E-CRM

Elham Abdulwahab Anaam - University Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
Su-Cheng Haw - Multimedia University, 63100, Cyberjaya, Malaysia
Palanichamy Naveen - Multimedia University, 63100, Cyberjaya, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.2-2.1043

Abstract


To create a personalized E-CRM recommendation system, the electronic customer relationship management system needs to investigate low accuracy and lack of personalization through applied hybrid recommendation system techniques such as fuzzy and AHP. The main purpose of this research is to enhance the accuracy and deep understanding of common recommendation techniques in E-CRM. The fuzzy and AHP techniques have been used in the current study to the available information of objects and to extend recommendation areas. The findings indicate that each of these strategies is appropriate for a recommendation system in a technological environment. The present study makes several noteworthy contributions to the fuzzy Analytic Hierarchy Process (AHP) and has the maximum accuracy of any of these approaches, with 66.67% of accuracy. However, AHP outperforms all others in terms of time complexity. We advocate the concept and implementation of an intelligent business recommendation system dependent on a hybrid approval algorithm that serves as a model for E–CRM recommendation systems. This recommendation system's whole design revolves on the hybrid recommendation system. The systems additionally incorporate the recommendation modules and the recommendation measurement updating framework. The recommendation modules include the formulation and development of material recommendation algorithms, element collaborative filtering recommendation algorithms, and demography-based recommendation algorithms.

Keywords


Hybrid recommendation technique; recommender system; E-CRM; Fuzzy-AHP; hybrid fuzzy.

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References


Anaam, E. A., Abu Bakar, K. A., Mohd Satar, N. S., & Ma’arif, M. Y. Investigating the Electronic Customer Relationship Management Success Key Factors in the Telecommunication Companies: A Pilot Study. Journal of Computational and Theoretical Nanoscience, 17(2-3), 1460-1463. 2020

Khan, R. U., Salamzadeh, Y., Iqbal, Q., & Yang, S. The impact of customer relationship management and company reputation on customer loyalty: The mediating role of customer satisfaction. Journal of Relationship Marketing, 21(1), 1-26. 2022,

Ardyan, E., & Sugiyarti, G. The influence of e-CRM capability and co-information sharing activity on product competitiveness and marketing performance of small and medium-sized enterprises. International Journal of Electronic Customer Relationship Management, 11(2), 158-178. 2018

Deldjoo, Y., Schedl, M., Cremonesi, P., & Pasi, G. Recommender systems leveraging multimedia content. ACM Computing Surveys (CSUR), 53(5), 1-38. 2020

Mezni, H., & Abdeljaoued, T. A cloud services recommendation system based on Fuzzy Formal Concept Analysis. Data & Knowledge Engineering, 116, 100-123. 2018

Anaam, E. A., Magableh, M. N. Y., & Ridha, A. Key Factors Influence on Decision Making to IoT Adoption in Telecommunication Companies: A Review. International Journal of Engineering & Technology, 11(1), 14-19. 2022

Khatter, H., Arif, S., Singh, U., Mathur, S., & Jain, S. Product recommendation system for E-commerce using collaborative filtering and textual clustering. In 2021 Third International Conference on Inventive Research in Computing Applications (CIRCA), 612-618). 2021 IEEE.

Ardyan, E., & Sugiyarti, G. The influence of e-CRM capability and co-information sharing activity on product competitiveness and marketing performance of small and medium-sized enterprises. International Journal of Electronic Customer Relationship Management, 11(2), 158-178, 2018

Anaam, E. A., Bakar, K. A. A., Satar, N. S. M., & Kamrul, M. Critical success factors for electronic customer relationship management success adoption: Telecommunication companies case study. International Journal of Advanced and Applied Sciences International Journal of Advanced and Applied Sciences, 8(10), 116-130. 2021

Koopialipoor, M., Jahed A, D., H, A., Marto, A., & Gordan, B. Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Computing, 23(14), 5913-5929. 2019

Anaam, E. A., Khairul, A., Abu Bakar, N. S., & Mohd, S. A theoretical review of a conceptual model for E-CRM success in telecommunication companies. International Journal of Engineering & Technology, 10. 2018

Anaam, E. A., Bakar, K. A. A., & Satar, N. S. M. A Model of Electronic Customer Relationship Management System Adoption In Telecommunication Companies. Amazonia Investiga, 9(35), 61-73. 2020

Jain, A., & Gupta, C. Fuzzy logic in recommender systems. In Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, pp. 255-273. Springer, Cham. 2018

Anaam, E.A., Magableh, M.N.Y., Hamdi, M., Hmoud, A.Y.R., & Alshalabi, H. Data Mining Techniques with Electronic Customer Relationship Management for Telecommunication Company. Amazonia Investiga, 10(48), 288-304. 2021, https://doi.org/10.34069/AI/2021.48.12.30

Davagdorj, K., Park, K. H., & Ryu, K. H. A collaborative filtering recommendation system for rating prediction. In Advances in intelligent information hiding and multimedia signal processing, 265-271, 2020.

Alshalabi, H., Tiun, S., Omar, N., Abdul Wahab Anaam, E., & Saif, Y. BPR algorithm: New broken plural rules for an Arabic stemmer. Egyptian Informatics Journal 2022.

Burke, Robin. “Hybrid Recommender Systems : Survey and Experiments, 1–29. 2018.

Ali, S., Khalid, N., Javed, H. M. U., & Islam, D. M. Z. Consumer adoption of online food delivery ordering (OFDO) services in Pakistan: The impact of the COVID-19 pandemic situation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 10, 2020.

Zhang, Q., Lu, J., & Jin, Y. Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7(1), 439-457. 2021.

Abderrahmane, D., Moulouki, R., Jihal, H., & Azzouazi, M. Architectural design of trust-based recommendation system in customer relationship management. Periodicals of Engineering and Natural Sciences, 6(2), 380-388. 2018.

Beshir, S., Labib, A., & Elkhouly, S. The Influence of Applying Mobile Customer Relationship Management (M-CRM) in the Service Sector on Employee’s Performance in Egypt. International Conference on Computer Theory and Applications (ICCTA), 122-127). 2020.

Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S. K. ICT-enabled CRM system adoption: a dual Indian qualitative case study and conceptual framework development. Journal of Asia Business Studies. 2020.

Koosha, H. R., Ghorbani, Z., & Nikfetrat, R. A Clustering-Classification Recommender System based on Firefly Algorithm. Journal of AI and Data Mining, 10(1), 103-116. 2022.

Anaam, E. A., Abu Bakar, K. A., Mohd Satar, N. S., & Ma’arif, M. Y. Investigating the Electronic Customer Relationship Management Success Key Factors in the Telecommunication Companies: A Pilot Study. Journal of Computational and Theoretical Nanoscience, 17(2-3), 1460-1463. 2020

Leong, L. Y., Hew, T. S., Ooi, K. B., & Chong, A. Y. L. Predicting the antecedents of trust in social commerce–A hybrid structural equation modeling with a neural network approach. Journal of Business Research, 110, 24-40. 2020.

Junior, Francisco Rodrigues Lima, Lauro Osiro, and Luiz Cesar Ribeiro Carpinetti. "A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection." Ap- plied Soft Computing 21: 194-209. 2014. DOI.org/10.1016/j.asoc.2014.03.014.

Hegde, K., Tsai, P. A., Huang, S., Chandra, V., Parashar, A., & Fletcher, C. W. Mind mappings: enabling efficient algorithm-accelerator mapping space search. In Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 943-958. 2021.

Billsus, D., & Pazzani, M. J. Learning collaborative information filters., 98, pp. 46-54. 1998.

Yang, Y., See-To, E. W., & Papagiannidis, S. You have not been archiving emails for no reason! Using big data analytics to cluster B2 B's interest in products and services and link clusters to financial performance. Industrial Marketing Management, 86, 16-29. 2020.

Anaam, E. A., Bakar, K. A. A., Satar, N. S. M., & Kamrul, M. Critical success factors for electronic customer relationship management success adoption: Telecommunication companies case study. International Journal of Advanced and Applied Sciences International Journal of Advanced and Applied Sciences, 8(10), 116-130. 2021.

Al-Ghamdi, M., Elazhary, H., & Mojahed, A. Evaluation of Collaborative Filtering for Recommender Systems. Evaluation, 12(3). 2021

Anaam, E. A., Khairul, A., Abu Bakar, N. S., & Mohd, S. A theoretical review of a conceptual model for E-CRM success in telecommunication companies. International Journal of Engineering & Technology, 10. 115. 2018

Sun, J., Zhang, Y., Ma, C., Coates, M., Guo, H., Tang, R., & He, X. Multi-graph convolution collaborative filtering. International Conference on Data Mining (ICDM), 1306-1311, 2019. IEEE.

Rodriguez, M., & Boyer, S. The impact of mobile customer relationship management (MCRM) on sales collaboration and sales performance. Journal of marketing analytics, 8(3), 137-148. 2020.