KEB173—Recommender System

  • Subburaj Ramasamy
  • A. Razia Sulthana


Trade and commerce are the fundamental necessities of the human beings. International trade existed centuries ago in the most cumbersome manner due to lack of communication facilities, and the lead times required were months and years. Today it happens in seconds owing to the availability of Information and communications technology abundantly at fingertips to everyone. The buyers would always like to consult their peers before taking a purchase decision. The advent of Internet has made it feasible to achieve this painlessly, get the reviews of other customers on the quality of the same or similar products online instantly. Recommender systems (RS) assess the information available on the Web and provide assistance to the users in making an informed selection of products or services. They assist the user in the purchase decision-making process. Research carried out by organizations such as Amazon on recommender systems is noteworthy. In the last few decades, a set of software tools and machine learning methodologies were proposed for implementing recommender systems. The user reviews are analyzed by knowledge discovery or machine learning techniques to extract and understand the pattern of purchase. The recommender systems handle the users online and assist them in choosing products meeting their requirements in consultation with peers worldwide at no cost and instantly. In this chapter, we give an overview about recommender systems and their nuances.


Collaborative filtering Content based Ontology based Context based Feature extraction Knowledge based 


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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information TechnologySRM Institute of Science and TechnologyKattankulathurIndia

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