Abstract
In this age of Internet and service delivery almost all the kinds of services and products are available online for selection and use. In addition of that for a single kind of product or service a number of different vendors and service providers are exist. Additionally all the providers are claimed to provide most valuable services. In this context to compare and find the appropriate service according to the end client a service recommendation system is required. The aim of this recommendation system design is to understand the client current requirements and explore the database for recovering the most likely services. In order to demonstrate the issues and solution of this domain a real-world problem namely hotel booking service is used. On the problem of this recommendation system design is treated as a search system on structured data source. Thus to find the suitable outcomes from the proposed working model quantum genetic technique is used. That technique first accepts the dataset information and the user requirements, after that the encoding of information is performed in binary values. Additionally the query sequence is treated as binary string with all 1s. Finally the genetic algorithm is implemented for finding the fit solution among all the available binary sequences. The generated seeds from the genetic algorithm are treated as final recommendation of search system. Additionally the fitness values are used to rank the solutions. The implementation and result evaluation is performed on JAVA technology. After that the performance using time and space complexity notified. Both the performance parameters demonstrate the acceptability of the work.
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
Similar content being viewed by others
References
Genetic algorithms for optimization, programs for MATLAB ® Version 1.0 User Manual
Xuan J, Jiang H, Ren Z Pseudo code of genetic algorithm and multi-start strategy based simulated annealing algorithm for large scale next release problem. Dalian University of Technology
Hu Rong, Dou Wanchun, Liu Jianxun (2014) ClubCF: A clustering-based collaborative filtering approach for big data application. IEEE Trans Emerg Topics Comput 2(3):302–313
Chuchra R (2012) Use of data mining techniques for the evaluation of student performance: a case study. Int J Comput Sci Manag Res 1(3)
Padhy N, Dr Mishra P (2012) The survey of data mining applications and feature scope. Int J Comput Sci Eng Inf Technol (IJCSEIT) 2(3), 43–58
Dunham MH, Sridhar S (2006) Data mining: introductory and advanced topics, 1st edn. Pearson Education, New Delhi. ISBN: 81-7758-785-4
Delmater R, Hancock M (2002) Data mining explained-a manager’s guide to customer-centric business intelligence. Digit Press, Boston
Bhatnagar A, Jadye SP, Nagar MM (2012) Data mining techniques & distinct applications: a literature review. Int J Eng Res Technol (IJERT) 1(9)
Industry application of data mining. http://www.pearsonhighered.com/samplechapter/0130862711.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Malviya, N., Jain, S. (2019). Web-Based Service Recommendation System by Considering User Requirements. In: Mishra, D., Yang, XS., Unal, A. (eds) Data Science and Big Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 16. Springer, Singapore. https://doi.org/10.1007/978-981-10-7641-1_25
Download citation
DOI: https://doi.org/10.1007/978-981-10-7641-1_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7640-4
Online ISBN: 978-981-10-7641-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)