Service Provider Allocation and Customers’ Service Requests Management System

Abstract

Automated technology revolutionizes the service industry and is a prerequisite for quality service. There are several ways to expand the use of automated systems, boost customer services, and save money in our day-to-day operations. The main focus of this research is to provide people with their service requirements and making day-to-day life easier. This research aims to automate customer service request management and the allocation of service providers. “UTILIQUE” is designed to provide customers a way of reaching out to service providers effectively and possible solutions if no service provider is located in the vicinity of the area. Besides, it analyzes user reviews and rank services providers accordingly, estimates operating costs for customers before delivering the services, and provides automated solutions. This is a mobile application and customers can easily control the application using voice and speech. Automated solutions providing, cost prediction, and ranking based on feedback analysis are used for market opportunities that can draw more users than other service providing applications.

Country : Sri Lanka

1 I.A.Kandamby2 A.W.S.G. Sasiprabha3 Perera K.G.I.D.4 Mahaadikara M.D.J.T Hansika5 Arachchi T.L.P.6 Devanshi Ganegoda

  1. Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  5. Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  6. Faculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 4, Issue 12, December 2020 pp. 28-34

doi.org/10.47001/IRJIET/2020.412005

References

  1. E. A. Portman et al., “Location-based services,” 6944447, 13-Sep-2005.
  2. Scherer, Anne & Wünderlich, Nancy & Wangenheim,Florian. (2015), “The Value of Self-Service: Long-Term Effects of Technology-Based Self-Service Usage on Customer Retention”, MIS Quarterly. 39. 177-200.10.25300/MISQ/2015/39.1.08.
  3. B. Maleki Shoja and N. Tabrizi, “Customer reviews analysis with deep neural networks for E-commerce recommender systems,” IEEE Access, vol. 7, pp. 119121–119130, 2019.
  4. L. Qiu, S. Gao, W. Cheng, and J. Guo, “Aspect-based latent factor model by integrating ratings and reviews for recommender system,” Knowl. Based Syst., vol. 110, pp. 233–243, 2016.
  5. K. Bauman, B. Liu, and A. Tuzhilin, “Aspect based recommendations: Recommending items with the most valuable aspects based on user reviews,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’17, 2017.
  6. X. Delman, Z. Shibeshi, and M. Scott, “Development of a Location Based Service for technician allocation,” in 2016 IST-Africa Week Conference, 2016, pp. 1–8.
  7. “Sentiment Analysis,” Lexalytics.com. [Online]. Available: https://www.lexalytics.com/technology/sentiment-analysis. [Accessed: 12-Nov-2020]
  8. M. Vibbert, J.-O. Goussard, R. J. Beaufort, and B. P. Monnahan, “Dialog flow management in hierarchical task dialogs,” 9767794, 19-Sep-2017.
  9. P. Kumar, M. Sharma, S. Rawat and T. Choudhury, "Designing and Developing a Chatbot Using Machine Learning," 2018 International Conference on System Modeling & Advancement in Research Trends (SMART),Moradabad, India, 2018, pp. 87-91, doi:10.1109/SYSMART.2018.8746972.
  10. M. Y. Helmi Setyawan, R. M. Awangga and S. R. Efendi, "Comparison Of Multinomial Naive Bayes Algorithm And Logistic Regression For Intent Classification In Chatbot," 2018 International Conference on Applied Engineering(ICAE), Batam, 2018, pp. 1-5, doi:10.1109/INCAE.2018.8579372
  11. Dongkeon Lee, Kyo-Joong Oh and Ho-Jin Choi, "The chatbot feels you - a counseling service using emotional response generation," 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju, 2017, pp. 437-440,doi: 10.1109/BIGCOMP.2017.7881752
  12. J. Li, X. Chen, E. Hovy, and D. Jurafsky, “Visualizing and understanding neural models in NLP,” arXiv [cs.CL], 2015.
  13. L. Breiman, “Random forests,” Machine Learning, vol. 45,no. 1, pp. 5–32, 2001.
  14. S. Malek, R. Gunalan, S. Kedija et al., “Random forest and Self Organizing Maps application for analysis of pediatric fracture healing time of the lower limb,” Neurocomputing, vol. 272, pp. 55–62, 2018.
  15. S. Wang, X. Liu, T. Yang, and X. Wu, “Panoramic crack detection for steel beam based on structured random forests,” IEEE Access, vol. 6, pp. 16432–16444, 2018.
  16. H. T. Malazi and M. Davari, “Combining emerging patterns with random forest for complex activity recognition in smart homes,” Applied Intelligence, vol. 48, no. 2, pp. 315–330, 2018.
  17. F. B. de Santana, A. M. de Souza, and R. J. Poppi, “Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 191, pp. 454–462, 2018.
  18. J. Abellán, C. J. Mantas, J. G. Castellano, and S. MoralGarcía, “Increasing diversity in random forest learning algorithm via imprecise probabilities,” Expert Systems with Applications, vol. 97, pp. 228–243, 2018.
  19. C. Hu, Y. Chen, L. Hu, and X. Peng, “A novel random forests based class incremental learning method for activity recognition,” Pattern Recognition, vol. 78, pp. 277–290, 2018.
  20. J. Abellán, C. J. Mantas, J. G. Castellano, and S. MoralGarcía, “Increasing diversity in random forest learning algorithm via imprecise probabilities”.
  21. H. M. Gomes, A. Bifet, J. Read et al., “Adaptive random forests for evolving data stream classification,” Machine Learning, vol. 106, no. 9-10, pp. 1469–1495, 2017.
  22. R. Genuer, J. Poggi, C. Tuleau-Malot, and N. Villa-Vialaneix,“Random forests for big data,” Big Data Research, vol. 9, pp.28–46, 2017.