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Web-Based Service Recommendation System by Considering User Requirements

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Data Science and Big Data Analytics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 16))

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.

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Correspondence to Neha Malviya .

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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

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