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Recommender System

Recommender System

Avinash Navlani, Nidhi Dadhich
Copyright: © 2017 |Pages: 22
ISBN13: 9781522521488|ISBN10: 1522521488|EISBN13: 9781522521495
DOI: 10.4018/978-1-5225-2148-8.ch011
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MLA

Navlani, Avinash, and Nidhi Dadhich. "Recommender System." Applying Predictive Analytics Within the Service Sector, edited by Rajendra Sahu, et al., IGI Global, 2017, pp. 176-197. https://doi.org/10.4018/978-1-5225-2148-8.ch011

APA

Navlani, A. & Dadhich, N. (2017). Recommender System. In R. Sahu, M. Dash, & A. Kumar (Eds.), Applying Predictive Analytics Within the Service Sector (pp. 176-197). IGI Global. https://doi.org/10.4018/978-1-5225-2148-8.ch011

Chicago

Navlani, Avinash, and Nidhi Dadhich. "Recommender System." In Applying Predictive Analytics Within the Service Sector, edited by Rajendra Sahu, Manoj Dash, and Anil Kumar, 176-197. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2148-8.ch011

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Abstract

With the increase in user choices and rapid change in user preferences, various methods required to capture such increasing choices and changing preferences. Online systems require quick adaptability. Another aspect is that with the increase in a number of items and users, computation time increases considerably. Thus system needs parallel computing platform to run newer designed recommender system techniques. Recommendation system helps people to tackle the choice overload problem and help to select the efficient one. Even though there is lots of work have been done in the recommendation system, still there is a problem in handling various types of data and basically to handle a large amount of data. The main aim of the recommendation system is to provide the best opinion from the available large amount of data. The present chapter describes an introduction to recommender systems, its functions, types, techniques, applications, collaborative filtering, content-based filtering and evaluation of performance.

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