Real-time recommender systems are critical for the success of the ecommerce industry. Newly developed deep neural networks (DNNs) have shown success as recommender systems by capturing the nonlinear relationships in the user-item dataset.
Kai Huang, Luyang Wang, and Jing Kong showcase how to build efficient real-time recommender systems to recommend products for different users by leveraging different types of DNNs. They use Analytics Zoo for Spark and BigDL to build the end-to-end flow on AWS at scale.
The system first processes all the transaction data and extract features on AWS; then it trains a comprehensive recommender model, including a neural collaborative filtering network, a wide and deep network, and a session-based recommender with recurrent neural networks. The system further serves the recommender model for each user on web service. By adopting the end-to-end flow of the Analytics Zoo solution, you can see a big improvement of accuracy compared to traditional recommendation algorithms.
本次议题讲师将解释如何使用基于Spark和BigDL的Analytics Zoo在AWS上大规模构建端到端流程。整个系统首先处理AWS上的所有交易数据并提取特征。然后，它训练了一个完整的推荐模型，包括神经协同过滤网络、宽且深的网络，以及基于会话的循环神经网络推荐器。这个系统进一步提供了推荐模型的服务，为网站上的每个用户提供推荐。通过采用Analytics Zoo解决方案的端到端流程，我们看到与传统推荐算法相比准确性有了大幅提升。
Kai Huang is a software engineer at Intel. His work mainly focuses on developing deep learning frameworks on Apache Spark and helping customers work out end to end deep learning solutions on big data platforms. He is a main contributor to Analytics Zoo and BigDL.
Kai Huang是英特尔软件工程师。 他的工作主要集中在开发Apache Spark深度学习框架，并帮助客户在大数据平台上制定端到端深度学习解决方案。他是Analytics Zoo和BigDL的主要贡献者之一。
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