TY - GEN
T1 - SHOPSPHERE - Recommendation system to develop a warehouse with personalized troupe of products
AU - Ignisha Rajathi, G.
AU - Prakalya, B. S.
AU - Nitin Sailesh, V.
AU - Sanchana, M.
AU - Anbu Gandhi, P.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the rapidly increasing technology and globalization, the internet has become an inevitable part of one's day-to-day life. The use of a sophisticated recommendation system has revolutionized the way consumers interact with online shopping platforms. To keep in pace with the emerging technology, every field needs to be digitalized and made up to date. In this context, the commercial shops and dealers too need to upgrade their traditional shopping methods. The old conventional methods don't always serve the new generation. The transition to online mode of shopping has made it mandatory for retailers and producers to customize according to the customer's requirement and providing various options than what was possible before. The focus in this system is to increase customer engagement, enhance the customer shopping experience, and boost sales by suggesting relevant products based on a user's browsing and purchase history, these products that are most likely to interest and satisfy individual customers. This paper reviews recent developments in recommendation systems in the domain of ecommerce.
AB - With the rapidly increasing technology and globalization, the internet has become an inevitable part of one's day-to-day life. The use of a sophisticated recommendation system has revolutionized the way consumers interact with online shopping platforms. To keep in pace with the emerging technology, every field needs to be digitalized and made up to date. In this context, the commercial shops and dealers too need to upgrade their traditional shopping methods. The old conventional methods don't always serve the new generation. The transition to online mode of shopping has made it mandatory for retailers and producers to customize according to the customer's requirement and providing various options than what was possible before. The focus in this system is to increase customer engagement, enhance the customer shopping experience, and boost sales by suggesting relevant products based on a user's browsing and purchase history, these products that are most likely to interest and satisfy individual customers. This paper reviews recent developments in recommendation systems in the domain of ecommerce.
UR - https://www.scopus.com/pages/publications/85190701166
UR - https://www.scopus.com/pages/publications/85190701166#tab=citedBy
U2 - 10.1109/ICRTAC59277.2023.10480838
DO - 10.1109/ICRTAC59277.2023.10480838
M3 - Conference contribution
AN - SCOPUS:85190701166
T3 - Proceedings of the 2023 6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023
SP - 287
EP - 292
BT - Proceedings of the 2023 6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023
A2 - Ganesan, R.
A2 - Harikrishnan, K.
A2 - Parvathi, R.
A2 - Geetha, S.
A2 - Thomas Abraham, J.V.
A2 - Vedhapriyavadhana, R.
A2 - Murugesan, Rajkumar
A2 - Kalaipriyan, T.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023
Y2 - 14 December 2023 through 15 December 2023
ER -