relation: https://khub.utp.edu.my/scholars/17273/
title: A Comprehensive Study on Recommendation Engines
creator: Chaudhari, A.
creator: Seddig, A.A.H.
creator: Sarlan, A.
creator: Raut, R.
description: Big Data (BD) is consistently participating in the recent computing revolution in an immense way. The volume of data generated through online platforms such as e-commerce portals comprises of huge hidden information which needs to be analyzed in-order to better serve customer's needs and retain their loyalty. Various Recommendation Engines (RE) have been proposed to tackle this problem and generate optimal recommendations based on user needs. This paper reviews and compares various types of RE highlighting their techniques, issues, applications, advantages and disadvantages. The paper also presents some results for different types of RE using sample datasets (Movie lens 100K) 12. © 2022 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Chaudhari, A. and Seddig, A.A.H. and Sarlan, A. and Raut, R.  (2022) A Comprehensive Study on Recommendation Engines.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147445188&doi=10.1109%2fICCUBEA54992.2022.10011001&partnerID=40&md5=da5e07aee5704e12d21cf0987564883e
relation: 10.1109/ICCUBEA54992.2022.10011001
identifier: 10.1109/ICCUBEA54992.2022.10011001