TY - GEN
T1 - Grand Challenge on Software and Hardware Co-Optimization for E-Commerce Recommendation System
AU - Li, Jianing
AU - Liu, Jiabin
AU - Hu, Xingyuan
AU - Zhang, Yuhang
AU - Yu, Guosheng
AU - Qian, Shimeng
AU - Mao, Wei
AU - Du, Li
AU - Li, Yongfu
AU - Du, Yuan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - E-commerce has become an indispensable part of the whole commodity economy with rapid expansion. A great deal of time is required for customers to search products by manual work. A good automatic recommendation system can not only bring the customers good shopping experience, but also help companies gain profit growth. In the IEEE AICAS 2023 conference, we have organized the grand challenge on software and hardware co-optimization for e-commerce recommendation system. The desensitized data from Alibaba Group which recorded online purchase behaviors of online shopping users in China are provided. We organize two rounds of the challenge with two different parts of data, separately encouraging participating teams to propose novel ideas for the recommendation algorithm design and deployment. In the preliminary round, participating teams are required to design a recommendation system with high accuracy performance. In the final round, the qualified teams from the preliminary round will be offered with an ARM-based multi-core Yitian 710 CPU cloud server, the teams are required to design an acceleration scheme for the hardware resolution. In the final, 6 best teams will be awarded by using standard evaluation criteria.
AB - E-commerce has become an indispensable part of the whole commodity economy with rapid expansion. A great deal of time is required for customers to search products by manual work. A good automatic recommendation system can not only bring the customers good shopping experience, but also help companies gain profit growth. In the IEEE AICAS 2023 conference, we have organized the grand challenge on software and hardware co-optimization for e-commerce recommendation system. The desensitized data from Alibaba Group which recorded online purchase behaviors of online shopping users in China are provided. We organize two rounds of the challenge with two different parts of data, separately encouraging participating teams to propose novel ideas for the recommendation algorithm design and deployment. In the preliminary round, participating teams are required to design a recommendation system with high accuracy performance. In the final round, the qualified teams from the preliminary round will be offered with an ARM-based multi-core Yitian 710 CPU cloud server, the teams are required to design an acceleration scheme for the hardware resolution. In the final, 6 best teams will be awarded by using standard evaluation criteria.
KW - Grand challenge
KW - efficient deployment
KW - machine-learning algorithms
KW - open-source
KW - recommendation system
KW - software and hardware co-optimization
UR - https://www.scopus.com/pages/publications/85166367994
U2 - 10.1109/AICAS57966.2023.10168648
DO - 10.1109/AICAS57966.2023.10168648
M3 - 会议稿件
AN - SCOPUS:85166367994
T3 - AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding
BT - AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023
Y2 - 11 June 2023 through 13 June 2023
ER -