Office Depot利用Analytics Zoo构建智能推荐系统的实践分享20200326_Kai Huang
1.Use Analytics Zoo to build an intelligent recommendation system on Office Depot Kai Huang Mar 26th, 2020
2. Outline ▪ Background and use case overview ▪ Introduction to Analytic Zoo ▪ Recommenders on Analytics Zoo ▪ Performance and deployment by Office Depot ▪ Conclusion
3. Why Recommendation Systems? ▪ Help customers choose from a variety of products. ▪ Maintain user satisfaction and royalty. ▪ Turn ordinary users into potential customers. ▪ Increase revenue per user visit. ▪ ……
4.Big Data Journey for Recommendation Stage I : Office Depot tried to build intelligent models for product recommendation using Python/SAS/R. Challenges: They can not process this amount of data on a single machine: ▪ Over 100,000,000 distinct sessions monthly. ▪ More than 300,000 active products selling online. ▪ Training data often exceed 10G.
5.Big Data Journey for Recommendation Stage II : Office Depot incorporated Spark into their workflow. Challenge: Deep learning libraries such as TensorFlow/Keras/PyTorch cannot run directly on Spark clusters. Why deep learning? ▪ Better performance on larger data. ▪ Less manual feature engineering needed. ▪ Easier to involve complex functions and combine different architectures.
6. Collaborative Filtering (ALS) ▪ The Collaborative filtering approach works by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices. ▪ Spark ALS (Alternating Least Squares) implementation runs matrix factorization in a parallel fashion and therefore has a pretty good scalability and performance.
7.Collaborative Filtering (ALS) Limitations of matrix factorization: ▪ Simple choice of the interaction function will hinder the performance. ▪ Data sparse problem. ▪ Not able to do incremental training. ▪ Cold start problem. ▪ Not able to capture the latest purchase intent. …
8. AI on Distributed, High-Performance A unified analytics and AI platform Deep Learning Framework for distributed Tensorflow, Keras, PyTorch and Ray for Apache Spark on Apache Spark https://github.com/intel-analytics/BigDL https://github.com/intel-analytics/analytics-zoo Accelerating Data Analytics + AI Solutions At Scale
9. Analytics Zoo Unified Big Data Analytics and AI Platform Models & Recommendation Time Series Computer Vision NLP Algorithms ML Workflow AutoML for Time Series Automatic Cluster Serving Integrated Distributed TensorFlow & PyTorch on Spark RayOnSpark Analytics & AI Pipelines Spark Dataframes & ML Pipelines for DL Model Serving Library & Distributions Distributed Analytics DL Frameworks Python Libraries Framework (Cloudera/Databricks/….) (Spark/Flink/Ray/…) (TF/PyTorch/…) (Numpy/Pandas/…) https://github.com/intel-analytics/analytics-zoo
10. Unified Big Data Analytics and AI Platform Seamless Scaling from Laptop to Production Prototype on laptop Experiment on clusters Production deployment w/ using sample data with history data distributed data pipeline Production Data pipeline • Easily prototype the integrated data analytics & AI solution • “Zero” code change from laptop to distributed cluster • Directly access production data (Hadoop/Hive/HBase) without data copy • Seamlessly deployed on production big data clusters
11. Real-World Applications NLP Based Customer Service Chatbot for Microsoft Azure* https://software.intel.com/en-us/articles/use-analytics-zoo-to-inject-ai-into-customer-service- platforms-on-microsoft-azure-part-1 https://www.infoq.com/articles/analytics-zoo-qa-module/ Industrial Product Defect Detection in Midea* https://software.intel.com/en-us/articles/industrial-inspection-platform-in-midea-and-kuka-using- distributed-tensorflow-on-analytics Unsupervised Time Series Anomaly Detection for Baosight* https://software.intel.com/en-us/articles/lstm-based-time-series-anomaly-detection-using-analytics- zoo-for-apache-spark-and-bigdl Any many more…
12.Neural Collaborative Filtering (NCF) ▪ NCF stimulates matrix factorization using DNN approach and is severed as a guideline for deep learning methods for recommendation services. ▪ It combines GMF with MLP to model user-item interactions. https://github.com/intel-analytics/analytics-zoo/tree/master/apps/recommendation-ncf
13. Wide & Deep Learning ▪ Wide and Deep learning model can take rich data as input. ▪ The wide part can effectively memorize sparse feature interactions using cross-product feature transformations. ▪ The deep part can generalize to previously unseen feature interactions through low dimensional user and item embeddings similar to NCF. https://github.com/intel-analytics/analytics-zoo/tree/master/apps/recommendation-wide-n-deep
14. Session Recommender ▪ Each user session in an e-commerce system could be modeled as a sequence of web pages. ▪ A deep RNN could track how users browse the website using multiple hidden layers. “Mouse” “Monitor” “Office Chair” “Desk” “Mouse” “Monitor”
15.Session Recommender The Good: ▪ Can catch the latest purchase intent from current session behavior and adjust its product recommendation in real time. ▪ Can work with both anonymous / identified customers. ▪ No pre-filtering mechanism required, simpler serving architect. The Bad: ▪ Sequence window size is hard to set. ▪ Online inference requires lots of resources.
16. Performance Comparison Offline measurement: Method Top 5 Accuracy Session Recommender 52.3% Wide & Deep 45.2% NCF 46.7% ALS 16.2% Online measurement: Online A/B testing shows the test group using Session Recommender lifted sales by 1% and average order value by 1.6% compared to control group. *Tested by Office Depot Note: test data provided by Office Depot
17.Recommendation System In Production Model Training (Yarn Cluster) ▪ Automated model deployment pipeline. ▪ No down time when update model in Maintain training code production. ▪ Ability to scale up / down according to the current workload using Kubernetes. Output model files Model Serving (Kubernetes Cluster) Model storage Request Load/update model files Collect new clickstream data LocalPredictor Real time prediction; post-filtering rules
18. Conclusion and Takeaways ▪ Analytics Zoo integrates deep learning well into existing big data pipelines. ▪ Analytics Zoo provides model serving API for high performance real-time inference. ▪ Deep learning based recommendation provides more flexibility to combine different model architectures for different use cases. ▪ Lots of NLP algorithms (for example, transformers) can be utilized for recommendation. ▪ Check out the joint blog for more information: https://software.intel.com/en-us/articles/real-time-product-recommendations-for- office-depot-using-apache-spark-and-analytics-zoo-on
19. Analytics Zoo on Ali E-MR https://help.aliyun.com/document_detail/93155.html + For more information and support, contact Wesley： Analytics Zoo is already out-of-box on Ali EMR： Email: firstname.lastname@example.org DingTalk: * Version upgrade for Analytics Zoo is on-going.
20. More Information on Analytics Zoo • Project websites • https://analytics-zoo.github.io/master/ • https://github.com/intel-analytics/analytics-zoo • https://github.com/intel-analytics/bigdl • Tutorials • CVPR 2018: https://jason-dai.github.io/cvpr2018/ • AAAI 2019: https://jason-dai.github.io/aaai2019/ • “BigDL: A Distributed Deep Learning Framework for Big Data” • In proceedings of ACM Symposium on Cloud Computing 2019 (SOCC’19) • https://arxiv.org/pdf/1804.05839.pdf • Use cases • Microsoft Azure, CERN, MasterCard, Baosight, Tencent, Midea, etc. • https://analytics-zoo.github.io/master/#powered-by/
21. Unified Analytics + AI Platform Distributed TensorFlow, Keras and BigDL on Apache Spark https://github.com/intel-analytics/analytics-zoo
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