Ray Summit

Mobile Order Click-Through Rate (CTR) Recommendation with Ray on Apache Spark at Burger King

Tuesday, June 22, 9:20PM UTC

Kai Huang, Software Engineer, Intel & Luyang Wang, Director of Machine Learning Engineering & Data Science, Restaurant Brands International

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For fast food recommendation, user behavior sequences and context features (such as time, weather, and location) are both important factors to be taken into consideration. At Burger King, we have developed a new state-of-the-art recommendation model called Transformer Cross Transformer (TxT). It applies Transformer encoders to capture both user behavior sequences and complicated context features and combines both transformers through the latent cross for joint context-aware fast food recommendations. Online A/B testings on mobile apps show that TxT can significantly lift the Click-Through Rate (CTR) compared with existing methods results. TxT has also been successfully applied to other fast food recommendation use cases outside of Burger King.

In addition, we have built an end-to-end recommendation pipeline leveraging Ray, Apache Spark and Apache MXNet, which integrates data processing (with Spark) and distributed training (with MXNet and Ray) into a unified data analytics and AI pipeline, running on the same big data cluster where the data is stored and processed. Such a unified system has been proven to be efficient, scalable, and easy to maintain in the production environment.

In this session, we will elaborate on our model topology and discuss the implementation details of our end-to-end recommendation pipeline. We will also share our practical experience in successfully building such a mobile order recommendation system with Ray and Spark on big data platforms.


Kai Huang

Kai Huang

Software Engineer, Intel

Kai Huang is a software engineer at Intel. His work mainly focuses on developing and supporting deep learning frameworks on Apache Spark. He has successfully helped many enterprise customers work out optimized end-to-end data analytics and AI solutions on big data platforms. He is a main contributor to open source big data + AI projects Analytics Zoo (https://github.com/intel-analytics/analytics-zoo) and BigDL (https://github.com/intel-analytics/BigDL).

Luyang Wang 2

Luyang Wang

Director of Machine Learning Engineering & Data Science, Restaurant Brands International

Luyang Wang is the director of machine learning engineering and data science at Restaurant Brands International, where he works on developing large scale recommendation systems and machine learning services for Burger King and Popeyes brand. Previously, Luyang Wang was working at the AI lab at Philips and Office Depot.