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
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Abstract

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.
电子商务推荐系统软硬件协同优化的重大挑战
电子商务迅速发展,成为整个商品经济不可缺少的组成部分。顾客通过手工搜索产品需要花费大量的时间。一个好的自动推荐系统不仅可以给顾客带来良好的购物体验,还可以帮助企业获得利润增长。在IEEE AICAS 2023会议上,我们组织了电子商务推荐系统软硬件协同优化的大挑战。本文提供了来自阿里巴巴集团的脱敏数据,记录了中国网购用户的网购行为。我们用两个不同部分的数据组织了两轮挑战赛,分别鼓励参赛团队为推荐算法的设计和部署提出新颖的想法。在初赛阶段,参赛团队需要设计出具有较高准确率的推荐系统。在最后一轮,初赛合格的团队将获得一台基于arm的多核亿天710 CPU云服务器,并要求团队设计硬件分辨率的加速方案。在决赛中,将根据标准评审标准选出6支最佳队伍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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