Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.

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Kwun-Hok Chan, Solution Architect
5 September 2018
Building a Recommender
System on AWS

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Let’s Build

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Amazon SageMaker
1
I
Notebook Instances
2
I
Algorithms
3
I
ML Training Service
4
I
ML Hosting Service

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github.com/chankh/sagemaker-fm-movielens

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Problem Framing
Data Exploration &
Preparation
Training
Optimization
Deployment

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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

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Collaborative Filtering
• Based on Item-to-Item
relationships
• Derived from Explicit and
Implicit features
• Recommendations are based
on other user’s experience

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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
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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

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What Should Our Solution Look Like?
UserId Liked MovieId
123 12 19 87 171
456 15 19 87 231
Likely
Recommendation

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Problem Framing
Data Exploration &
Preparation
Training
Optimization
Deployment

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Our Data Set: Movielens
• Public Data Set produced by GroupLens Research
• https://grouplens.org/datasets/movielens/

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Item Information

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User Information

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Visualizing The Data
Total Feature Count = Users +
Movies
= 2625 Features

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Visualizing The Data
Minimum rating
count per user: 20

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Data Preparation: Binary Classification
Not Liked Liked

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Matrix Factorization With Factorization Machines
Rating Matrix
≈
User
Matrix
Item
Matrix
𝑘
𝑘×

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Data Preparation: One-Hot Encoding
Users Movies
0 1 0 0 0 0 0 0 0 1 0 0 0 0
Rating
1
UserId MovieId Rating
2 4 5

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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

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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

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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

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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

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Problem Framing
Data Exploration &
Preparation
Training
Optimization
Deployment

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Training – Single Instance

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Training – Multiple Instances

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Evaluating The Model
F1.000 Score
73.8%

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SageMaker
Notebooks
Training
Algorithm
SageMaker
Training
Prepare
Training Data
Amazon S3
Amazon S3
Train & Optimize
Raw
Data
Prepared
Data
Trained
Model
Docker
Container
Model Training

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Problem Framing
Data Exploration &
Preparation
Training
Optimization
Deployment

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Optimization Approaches
Add higher order
features
Hyperparameter
Optimization
Hybrid
Models

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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

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Hyperparameters

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Hyperparameter Optimization
Apply Machine Learning to optimize model training hyperparameters
F1.000 Score
77.2% (+3.5%)
Optimized
Hyperparameters

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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

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Problem Framing
Data Exploration &
Preparation
Training
Optimization
Deployment

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SageMaker
Notebooks
Training
Algorithm
SageMaker
Training
SageMaker
Hosting
Prepare
Training Data
Amazon S3
Amazon S3
Train & Optimize Deploy
Raw
Data
Prepared
Data
Algorithm
Container
Trained
Model
Trained
Model
HPO
Deploying An Endpoint

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Calling SageMaker Endpoints
• Understand the inference request format for your algorithm
• Factorization Machines support JSON & protobuf
• Sample JSON Payload:

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Integrating Endpoints With Applications
API
Gateway
SageMaker
Endpoint
Lambda
Function
Client

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Endpoint Invocation

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Enriching The Endpoint Response
Endpoint
Response
Enriched
with MovieId

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SageMaker
Notebooks
Training
Algorithm
SageMaker
Training
SageMaker
Hosting AWS
Lambda
API
Gateway
Prepare
Training Data Inference requests
Amazon S3
Amazon S3
Train & Optimize Deploy
Raw
Data
Prepared
Data
Algorithm
Container
Trained
Model
Trained
Model
HPO
User
Interactions
Solution Architecture

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Where To From Here?
- Amazon SageMaker:
https://aws.amazon.com/sagemaker/developer-resources/
- SageMaker Automatic Model Tuning:
https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-
model-tuning.html

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Your Feedback is Important!
http://bit.ly/2Cjblrc

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Thank You!
@kwunhok