User-based recommendation systems have become an important topic in ecommerce. Traditional approaches like rule-based systems can’t consider user-specific characters and the implicit relationship between users and products to provide customized recommendation solutions. The newly developed deep neural networks have shed light on a path to success by chaptering nonlinear relationships in the user-item dataset, leveraging user, product attributes, and user-item interaction history. With the huge dataset a big ecommerce company commands, a training model in the cloud is a more achievable and efficient solution.
BigDL, a new distributed deep learning framework on Apache Spark, provides easy and seamlessly integrated big data and deep learning capabilities for big data users and data scientists. Analytics Zoo is an analytics and AI platform for Spark and BigDL; it helps users build and productionize deep learning apps for big data at scale. The deep learning algorithms in BigDL result in much better results compared to traditional recommendation algorithms. Lu Wang, Nicole Kong, Guoqiong Song, and Maneesha Bhalla explain how to build a user-based recommendation system with neural collaborative filtering and wide and deep models using Analytics Zoo with BigDL on Apache Spark in the cloud. They then demonstrate how to deploy the model and serve the real-time user-based recommendation on a website.
Luyang Wang is a senior manager on the Burger King guest intelligence team at Restaurant Brands International, where he works on machine learning and big data analytics. He’s engaged in developing distributed machine learning applications and real-time web services for the Burger King brand. Previously, Luyang Wang was at Philips Big Data and AI Lab and Office Depot.
Jing (Nicole) Kong is a data scientist at Office Depot, where she deals with big data and transforms data and models into products and service that drive business. She’s experienced with a number of different machine learning and deep learning models.
Guoqiong Song is a senior deep learning software engineer on the big data technology team at Intel. She’s interested in developing and optimizing distributed deep learning algorithms on Spark. She holds a PhD in atmospheric and oceanic sciences with a focus on numerical modeling and optimization from UCLA.
Guoqiong Song是英特尔大数据技术团队的高级深度学习软件工程师。 她拥有加州大学洛杉矶分校的大气和海洋科学博士学位，专业方向是数值建模和优化。 她现在的研究兴趣是开发和优化分布式深度学习算法。
Maneesha Bhalla is director of advanced analytics at Office Depot. A thought leader with 16+ years of experience in data analytics and data science specifically in customer analytics, Maneesha is passionate about data-driven approaches to turn data into insights and has a proven ability to build and lead highly efficient teams to enable the organization’s strategic priorities. Maneesha holds a certificate from the Executive Management Program in Global Business Management at IIM Calcutta and a bachelor’s degree in chemical engineering from Pune University.
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