TY  - CONF
AV  - none
N2  - 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.
N1  - 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
ID  - scholars17273
TI  - A Comprehensive Study on Recommendation Engines
KW  - Big data; Collaborative filtering; Engines; Machine learning
KW  -  Collaborative filtering component; Content based filtering; Customer need; E- commerces; Hidden information; Machine-learning; Online platforms; Sample dataset; User need
KW  -  Recommender systems
Y1  - 2022///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781665484510
A1  - Chaudhari, A.
A1  - Seddig, A.A.H.
A1  - Sarlan, A.
A1  - Raut, R.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147445188&doi=10.1109%2fICCUBEA54992.2022.10011001&partnerID=40&md5=da5e07aee5704e12d21cf0987564883e
ER  -