Presented By

June 18-21, 2019
Beijing, CN

Real-time product recommendations leveraging deep learning on Apache Spark in Office Depot

此演讲使用中文 (This will be presented in Chinese)

Guoqiong Song (Intel), Luyang Wang (Office Depot), Jiao(Jennie) Wang (Intel), Jing (Nicole) Kong (Office Depot)
14:5015:30 Thursday, June 20, 2019
实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
Location: 多功能厅5A+B(Function Room 5A+B)

必要预备知识 (Prerequisite Knowledge)

A basic understanding of Apache Spark, machine learning, and deep learning

您将学到什么 (What you'll learn)

• Explore a run-time recommendation system built with deep learning using Analytics-Zoo on BigDL and Apache Spark • Gain insight into the process for developing a full end-to-end deep learning workflow including elements of big data and machine learning in the cloud.

描述 (Description)

Real-time recommender systems are critical for the success of e-commerce industry. Newly developed Deep Neural Networks (DNNs) have shown success as recommender systems by capturing the non-linear relationships in the user-item dataset. This talk will illustrate how to build efficient real-time recommender systems to recommend products for different users by leveraging different types of DNNs.

This presentation will explain how to build the end-to-end flow on AWS at scale, using Analytics Zoo for Spark and BigDL. The system first processes all the transaction data and extract features on AWS; then it trains a comprehensive recommender model including Neural collaborative filtering network, 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 Analytics Zoo solution, we saw about big improvement of accuracy compared to traditional recommendation algorithms.

Photo of Guoqiong Song

Guoqiong Song


Guoqiong Song is a senior deep learning software engineer of the big data technology team at Intel. She has a PhD degree in atmospheric and oceanic sciences from UCLA, with a focus on numerical modling and optimization. Her interest is in developing and optimizing distributed deep learning algorithms on spark

Photo of Luyang Wang

Luyang Wang

Office Depot

Lu is a data scientist / big data engineer from OfficeDepot, where he works on machine learning and big data analytics. He is engaged in developing distributed machine learning applications and real-time web services for OfficeDepot digital business platform.

Photo of Jiao(Jennie) Wang

Jiao(Jennie) Wang


Jiao (Jennie) Wang is a software engineer on the big data technology team at Intel, where she works in the area of big data analytics. She is engaged in developing and optimizing distributed deep learning framework on Apache Spark.

Photo of Jing (Nicole) Kong

Jing (Nicole) Kong

Office Depot

Jing(Nicole) is a data scientist experienced with different machine learning/deep learning model and deals with big data and transform data/model into products and service that drive business.

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