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DSS for Web Mining Using Recommendation System

DSS for Web Mining Using Recommendation System

Varaprasad Rao M, Vishnu Murthy G
Copyright: © 2017 |Pages: 13
ISBN13: 9781522518778|ISBN10: 1522518770|EISBN13: 9781522518785
DOI: 10.4018/978-1-5225-1877-8.ch003
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MLA

Varaprasad Rao M, and Vishnu Murthy G. "DSS for Web Mining Using Recommendation System." Web Data Mining and the Development of Knowledge-Based Decision Support Systems, edited by G. Sreedhar, IGI Global, 2017, pp. 22-34. https://doi.org/10.4018/978-1-5225-1877-8.ch003

APA

Varaprasad Rao M & Vishnu Murthy G. (2017). DSS for Web Mining Using Recommendation System. In G. Sreedhar (Ed.), Web Data Mining and the Development of Knowledge-Based Decision Support Systems (pp. 22-34). IGI Global. https://doi.org/10.4018/978-1-5225-1877-8.ch003

Chicago

Varaprasad Rao M, and Vishnu Murthy G. "DSS for Web Mining Using Recommendation System." In Web Data Mining and the Development of Knowledge-Based Decision Support Systems, edited by G. Sreedhar, 22-34. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1877-8.ch003

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Abstract

Decision Supports Systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem. A DSS is an interactive computer based information system with an organized collection of models, people, procedures, software, databases, telecommunication, and devices, which helps decision makers to solve unstructured or semi-structured business problems. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web mining can be divided into three different types – Web usage mining, Web content mining and Web structure mining. Recommender systems (RS) aim to capture the user behavior by suggesting/recommending users with relevant items or services that they find interesting in. Recommender systems have gained prominence in the field of information technology, e-commerce, etc., by inferring personalized recommendations by effectively pruning from a universal set of choices that directed users to identify content of interest.

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