eprintid: 17273
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/72/73
datestamp: 2023-12-19 03:23:41
lastmod: 2023-12-19 03:23:41
status_changed: 2023-12-19 03:07:46
type: conference_item
metadata_visibility: show
creators_name: Chaudhari, A.
creators_name: Seddig, A.A.H.
creators_name: Sarlan, A.
creators_name: Raut, R.
title: A Comprehensive Study on Recommendation Engines
ispublished: pub
keywords: Big data; Collaborative filtering; Engines; Machine learning, Collaborative filtering component; Content based filtering; Customer need; E- commerces; Hidden information; Machine-learning; Online platforms; Sample dataset; User need, Recommender systems
note: cited By 0; Conference of 6th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2022 ; Conference Date: 26 August 2022 Through 27 August 2022; Conference Code:186077
abstract: 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.
date: 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147445188&doi=10.1109%2fICCUBEA54992.2022.10011001&partnerID=40&md5=da5e07aee5704e12d21cf0987564883e
id_number: 10.1109/ICCUBEA54992.2022.10011001
full_text_status: none
publication: 2022 6th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2022
refereed: TRUE
isbn: 9781665484510
citation:   Chaudhari, A. and Seddig, A.A.H. and Sarlan, A. and Raut, R.  (2022) A Comprehensive Study on Recommendation Engines.  In: UNSPECIFIED.