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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: wesley.du@intel.com
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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information about performance and benchmark results, visit http://www.intel.com/performance.
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© 2019 Intel Corporation
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