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.
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
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.
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.
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.
Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?
Join the conversation here (requires login)
©2019, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • email@example.com