Building a Recommender System on AWS Delivering personalised, relevant content can help to grow your audience, improve customer experience and drive higher sales. In this session we will explore different techniques for implementing Recommender Systems, from simple metrics based suggestions to more complex solutions using AI and Machine Learning. We will dive into some public data sets and demonstrate how you can build a Recommender System using AWS services. Alastair Cousins, Solutions Architect, Amazon Web Services

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Alastair Cousins
Senior Solutions Architect, Amazon Web Services
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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Problem Framing
Data Exploration &
Preparation
Training
Optimisation
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
Likely
Recommendation

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Problem Framing
Data Exploration &
Preparation
Training
Optimisation
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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Visualising The Data
Total Feature Count = Users + Movies
= 2625 Features

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Visualising The Data
Minimum rating
count per user: 20

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Data Preparation: Binary Classification
Not Liked Liked

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

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

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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
Optimisation
Deployment

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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

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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 & Optimise
Raw
Data
Prepared
Data
Trained
Model
Docker
Container
Model Training

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Problem Framing
Data Exploration &
Preparation
Training
Optimisation
Deployment

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Optimisation Approaches
Add higher order
features
Hyperparameter
Optimisation
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 Optimisation
Apply Machine Learning to optimise model training hyperparameters
F1.000 Score
77.2% (+3.5%)
Optimised
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
Optimisation
Deployment

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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

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Calling SageMaker Endpoints
• Understand the inference request format for your algorithm
• Factorisation 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 & Optimise Deploy
Raw
Data
Prepared
Data
Algorithm
Container
Trained
Model
Trained
Model
HPO
User
Interactions
Solution Architecture

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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

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Thank You