Abstract
In the realm of computer science, RSS is a set of tools and methods for making useful product recommendations to end users. To maintain footholds in competitive industry, telecoms provide a wide range of offerings. It is challenging for a client to choose the best-fit product from the huge bouquet of products available. It is possible to increase suggestion quality by using the large amounts of textual contextual data detailing item qualities which are accessible with rating data in various recommender’s domains. Users have a hard time making purchases in the telecom industry. Here, fresh strategy for improving recommendation systems in the telecommunications industry is proposed. Users may choose the recommended services which is loaded onto their devices. Using a recommendation engine is a simple way for telecoms to increase trust and customer satisfaction index. The suggested recommendation engine allows users to pick and choose services they need. The present study compared two distinct recommendation frameworks: a single algorithm and an ensemble algorithm model. Experiments were conducted to compare the efficacy of separate algorithms and ensemble algorithm. Interestingly, the ensemble algorithm-based recommendation engine has proven to provide better recommendations in comparison to individual algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Singh, I., Singh, K.V., Singh, S.: Big data analytics based recommender system for value added services (VAS). Adv. Intell. Syst. Comput.
Wilson J, Chaudhury, S., Kapadia, P., Lall, B.: Improving collaborative filtering based recommenders using topic modelling. In: Proceedings Cornell University (2014)
Soft, M., Zulu, D.M., Mazhandu, R.: Recommender system for telecommunication industries: a case of Zambia telecoms. Am. J. Econ., 271–273 (2017)
Zhang, Z., Lin, H., Liu, K., Wu, D., Zhang, G., Lu, J.: A hybrid fuzzy-based personalized recommender system for telecom products/services. Inf. Sci. 235, 117–129 (2013)
Thomas, S., Wilson, J., Chaudhury, S.: Best-fit mobile recharge pack recommendation. In: 2013 National Conference on Communications (NCC) (2013)
Morozov, S., Zhong, X.: The evaluation of similarity metrics in collaborative filtering recommenders. In: Hawaii University International Conferences, Hawaii (2013)
Shirkhorshidi, S., Aghabozorgi, S., Wah, T.Y.: A comparison study on similarity and dissimilarity measures in clustering continuous data. PLoS ONE 10(12), e0144059 (2015)
Jain, K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)
Perlibakas, V.: Distance measures for PCA-based face recognition. Pattern Recogn. Lett. 25(6), 711–724 (2004)
Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: a survey. IEEE Trans. Knowl. Data Eng. 11, 1370–1386 (2004)
Bhatt, B., Patel, P.J., Gaudani, H.: A review paper on machine learning based recommendation system. 2014 IJEDR 2(4). ISSN: 2321-9939
Cleger-Tamayo, S, Fernandez-Luna, J.M., Huete, J.F.: A new ´ criteria for selecting neighborhood in memory-based recommender systems. In: Proceedings of the 14th international conference on Advances in artificial intelligence: Spanish association for artificial intelligence, ser. CAEPIA’11, pp. 423–432. Springer, Berlin (2011)
Kridel, D.J., Dolk, D.R., Castillo, D.: Recommender systems as a mobile marketing service. J. Serv. Sci. Manage. 6, 32–48 (2008). Published Online December 2013 (http://www.scirp.org/journal/jssm)
Candillier, L., Meyer, F., Boulle, M.: Comparing state-of-the-art collaborative filtering systems. In: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition, ser. MLDM ’07, pp. 548–562. Springer, Berlin (2007)
Zauvik, Laksito, A.D.: The comparison of distance measurement for optimizing KNN collaborative filtering recommender system. In: 3rd International Conference of Information and Communications Technology (2020)
Bobadilla, J., Serradilla, F., Bernal, J.: A new collaborativefiltering metric that improves the behavior of recommender systems. Knowl. Based Syst. 23(6), 520–528 (2010)
Brun, Castagnos, S., Boyer, A.: A positively directed mutual information measure for collaborative filtering.In: 2nd International Conference on Information Systems and Economic Intelligence—SIIE 2009, pp. 943–958 (2009)
Gualdi, S., Medo, M., Zhang, Y.-C.: Crowd avoidance and diversity in socio-economic systems and recommendation (2013)
Boe, “Collaborative filtering for recommending movies,” Master’s thesis, Norwegian University of Science and Technology (2007)
Wit, J.: “Evaluating recommender systems,” Master’s thesis, University of Twente, May (2008)
Yongli Ren, W.Z., Li, G.: Automatic generation of recommendations from data: a multifaceted survey. Deakin University, Tech. Rep. TR C11/4 (2011)
Hu, B., Long, Z.: Collaborative filtering recommendation algorithm based on user explicit preference. In: 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) (2021). https://doi.org/10.1109/ICAICA52286.2021.9498149
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Roy, D.R., Sinha, S.K., Veenadhari, S. (2023). Big Data Analytics-Based Recommendation System Using Ensemble Model. In: Bhattacharyya, S., Das, G., De, S., Mrsic, L. (eds) Recent Trends in Intelligence Enabled Research. DoSIER 2022. Advances in Intelligent Systems and Computing, vol 1446. Springer, Singapore. https://doi.org/10.1007/978-981-99-1472-2_8
Download citation
DOI: https://doi.org/10.1007/978-981-99-1472-2_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1471-5
Online ISBN: 978-981-99-1472-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)