Recommender System

Recommender System

Avinash Navlani (Devi Ahilya Vishwavidyalaya, India) and Nidhi Dadhich (Devi Ahilya Vishwavidyalaya, India)
Copyright: © 2017 |Pages: 22
DOI: 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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Recommender System

With the dawn of the internet, a resource to recover and utilize the information has become pervasive but information is growing exponentially. The problem arises here, how to find the right information which meets customer needs. In this context, Recommender System can save us from going through such huge information. The rapid development of e-business market leads to increase the amount of information available. Customers were finding it very hard to achieve the right decision from this overloaded information. This flood of information creates a problem in decision making.

E-business ventures are instead of producing benefits, started losing users' due to high load of information. The choices are always good but more choices are not always better. The recommendation is generated based upon the data about user, types of knowledge, previous transactions then the following step is implicit/ explicit feedback input. Each user action and feedback kept in a dataset and utilized in next recommendation. A recommender system is new research area as compared to other information tools and technique.

Figure 1.

Recommender System

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