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Grand Challenge on Software and Hardware Co-Optimization for E-Commerce Recommendation System.

Jianing Li, Jiabin Liu, Xingyuan Hu,Yuhang Zhang, Guosheng Yu, Shimeng Qian,Wei Mao,Li Du,Yongfu Li,Yuan Du

AICAS(2023)

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摘要
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.
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关键词
Grand challenge,software and hardware co-optimization,recommendation system,open-source,efficient deployment,machine-learning algorithms
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