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).
May 26, 2021 05:00 PM PT
Offers are important sales drivers in the fast-food industry. Being able to segment customers based on their offer preferences and assigning the best offer sets to each segment is critical for a customer-centric recommendation system to enhance user shopping experience.
Directly applying recommendation models to this use case would cause the “black box” problem where marketers can have difficulty understanding the model’s decision logic. It’s also challenging for marketers to apply different offer strategies to different customers. On the other hand, traditional customer segmentation approaches won't generate offer recommendations and therefore they still rely on marketers to manually assign offers to each user segment.
At Burger King, we have developed our own offer recommendation system that leverages pre-trained BERT and Inception models on Apache Spark to extract feature representations directly from offer descriptions and images, followed by Spark MLlib to form the user segmentations. Such a system can discover customer segmentations based on their offer preference and can also directly generate offer recommendations from these resulting segments.
Furthermore, it is robust enough to handle the constantly changing offer pool and newly joined customers without the need to keep retraining the model from time to time. In this session, we would discuss our offer recommendation system in detail.
November 17, 2020 04:00 PM PT
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
June 24, 2020 05:00 PM PT
With the rapid evolution of AI in recent years, we need to embrace advanced and emerging AI technologies to gain insights and make decisions based on massive amounts of data. Ray (https://github.com/ray-project/ray) is a fast and simple framework open-sourced by UC Berkeley RISELab particularly designed for easily building advanced AI applications in a distributed fashion. Nevertheless, it is not straightforward for Ray to directly deal with big data, especially the data from real-life production environment. Instead of running big data applications and AI applications on two separate systems, we hereby introduce our work for RayOnSpark, which could gracefully allow users to run Ray programs on big data platforms. In this session, we will discuss our implementation of RayOnSpark in detail. You will have an intuitive understanding on how to run various emerging AI applications (including distributed training of deep neural networks, scalable AutoML for time series prediction, distributed reinforcement learning, etc.) on Apache Hadoop/YARN clusters by utilizing Ray and RayOnSpark. In addition, RayOnSpark allows Ray programs to be seamlessly integrated with Apache Spark data processing pipelines and directly run on in-memory Spark RDDs or DataFrames to eliminate expensive data transfer overhead among different systems.