For fast food recommendation use cases, 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 show not only the superiority of TxT comparing to existing methods results but also TxT can be successfully applied to other fast food recommendation use cases outside of Burger King.
In addition, we have built an end-to-end recommendation system leveraging Ray (https://github.com/ray-project/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 cluster where our big 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 would elaborate on our model topology and the architecture of our end-to-end recommendation system in detail. We are also going to share our practical experience in successfully building such a recommendation system on big data platforms.
Speakers: Kai Huang and Luyang Wang
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).
Burger King Corporation
Luyang Wang is a Sr. Manager on the Burger King data science team at Restaurant Brands International, where he works on developing large scale recommendation systems and machine learning services. Previously, Luyang Wang was working at the AI lab at Philips and Office Depot.