Building a Recommender System on AWS - AWS Summit Sydney 2018

9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Content-Based Filtering UserId Rock Jazz HipHop Classical 7653 5 2 3 1 Generate Recommendations based on known user preferences • Easy to understand and implement • Applicable to domains where user preferences can be captured in full • Cannot predict new user preferences

10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Collaborative Filtering • Based on Item-to-Item relationships • Derived from Explicit and Implicit features • Recommendations are based on other user’s experience

11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. What Should Our Solution Look Like? UserId Liked MovieId 123 12 19 87 171 456 15 19 87 231 Movies 19, 87 are liked by multiple users

22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data Preparation: One-Hot Encoding UserId MovieId Rating 2 4 5 2 7 2 Users Movies 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 Rating 1 0

23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data Preparation: One-Hot Encoding UserId MovieId Rating 2 4 5 2 7 2 4 5 4 Users Movies 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 Rating 1 0 1

24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Sparse Data • One-Hot Encoding produces a a 2625x90,570 matrix for our training set • This data set is 99.92% zeros. • Use a memory efficient data structure: scipy.lil_matrix Users Movies 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 Rating 1 0 1

25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. SageMaker Notebooks SageMaker Training Prepare Training Data Amazon S3 Raw Data Prepared Data 1. Import Data 2. Identify and Enrich Features 3. Format data for training 4. Write out data for distributed training Data Preparation

27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Log_loss F1 Score Seconds SageMaker 0.494 0.277 820 Other (10 Iter) 0.516 0.190 650 Other (20 Iter) 0.507 0.254 1300 Other (50 Iter) 0.481 0.313 3250 Click Prediction 1 TB advertising dataset, m4.4xlarge machines, perfect scaling. $- $50.00 $100.00 $150.00 $200.00 1. 2.75 4.5 6.25 8. CostinDollars Billable Time in Hours 10 machines 20 machines 30 machines 4050 Factorisation Machines

31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. SageMaker Notebooks Training Algorithm SageMaker Training Prepare Training Data Amazon S3 Amazon S3 Train & Optimise Raw Data Prepared Data Trained Model Docker Container Model Training

34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Higher Order Features • Adding additional features can improve accuracy • Additional Features help with Cold Start suggestions • Select features by experimentation Users Movies 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 Genres 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0

36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Hyperparameter Optimisation Apply Machine Learning to optimise model training hyperparameters F1.000 Score 77.2% (+3.5%) Optimised Hyperparameters

37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Hybrid Models 1. Cluster individual users into groups 2. Sort prediction data sets based on genres 3. Generate predictions using the clustered user and filtered prediction data set that aligns best to the application context Horror Fans Age 8-10 New Signups Comedies New Releases Animation

39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. SageMaker Notebooks Training Algorithm SageMaker Training SageMaker Hosting Prepare Training Data Amazon S3 Amazon S3 Train & Optimise Deploy Raw Data Prepared Data Algorithm Container Trained Model Trained Model HPO Deploying An Endpoint

40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Calling SageMaker Endpoints • Understand the inference request format for your algorithm • Factorisation Machines support JSON & protobuf • Sample JSON Payload:

44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. SageMaker Notebooks Training Algorithm SageMaker Training SageMaker Hosting AWS Lambda API Gateway Prepare Training Data Inference requests Amazon S3 Amazon S3 Train & Optimise Deploy Raw Data Prepared Data Algorithm Container Trained Model Trained Model HPO User Interactions Solution Architecture

45. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Where To From Here? - Sample Code: https://medium.com/@julsimon/building-a-movie- recommender-with-factorization-machines-on-amazon-sagemaker- cedbfc8c93d8 - SageMaker HPO Preview: https://pages.awscloud.com/amazon- sagemaker-hpo-preview.html