The document outlines the author's experience and expertise in data science with a focus on e-commerce applications, covering topics like recommendation systems, consumer behavior modeling, and deep learning for image analysis. It details various models used for consumer behavior analysis and different types of recommendation systems, including content-based and collaborative filtering methods. The author also emphasizes the significance of deep learning techniques in product recommendation, image classification, and enhancing user experience in e-commerce.

1. Applied
Data Science
for E-Commerce

2. Hello!
I am Arul Bharathi
◇ Data Scientist Intern @ Realtor.com
◇ Master’s in Big Data, Simon Fraser University
◇ PG Diploma in Data Analytics
◇ 5 years in Banking & Financial Services (BI & Data Science)
@linkedin.com/in/arulbharathi

3. Interests
◇ Deep Learning - Computer Vision
◇ ML Engineering & Deployment
◇ Photography & Blogging
Vision
◇ Non-profit VNC DL Community
◇ Crowdfund and co-learn with DL Enthusiasts
◇ Solving Complex Problems

4. Why we are all here?
● Data Science Use Cases in E-Commerce
● Recommendation Systems - Walkthrough
● Consumer Behavioral Modelling - RF
● Deep Learning
○ Image Recommendation
○ Image Similarity

5. Data Science Use Cases
in E-Commerce1

6. Use Case Types of Models
Consumer Behavior Analysis Tree-based Models - Interpretation
User Experience Recurrent Neural Networks, Language Models
Product Category Classification Deep Learning - Image Classification, Object
Detection
Product Recommendation Deep Learning - Image Regression, NLP,
Recommendation Systems

7. Recommendation
Systems - Walkthrough2

8. Types of Recommendation Systems
Recommender
Systems
Content Based
Filtering
Collaborative
Filtering
Item BasedUser Based
Hybrid Methods

9. Content Based Filtering
● Main Idea - Recommend items to Customer X similar to previous
items rated highly by X

10. Pros
● No need of data on other users
● Able to recommend users with unique tastes
● Able to recommend new and unpopular items
● Able to provide explanations
Cons
● How to build a item profile for new users?
● Unable to exploit quality judgements of other users
● Overspecialization

11. Collaborative Filtering
● Consider user X
● Find set N of other users whose ratings are similar to x’s ratings
● Estimate x’s ratings based on the ratings of users in N

12. User Based Collaborative Filtering
recommended

13. Prescribe it to you
Prescribe the
movies with high
rating predictions
Find Similar Users
Similar users to
you is found using
correlation metrics
Predict Ratings
Predict the ratings
those users would
give for products

14. Item Based Collaborative Filtering
Similar Items
Higher
Predicted
Rating
recommendation

15. Item Based Collaborative Filtering
Prescribe it to you
Prescribe the item
based on rating
metrics
Find Similar Items
Find Similar items
to the item that
has to be
recommended to
the user
Predict Ratings
Predict the user
rating for the item
using weighted
average

16. Pros
● Works for any kind of item, no feature selection needed
Cons
● Cold Start - Need enough users in the system to start
● Sparsity - Hard to find users that have rated the same item
● Popularity Bias - Cannot recommend items with unique taste

17. Consumer Behavior
Modelling3

18. DATA AGGREGATION
FEATURE
EXTRACTION
MODEL
INTERPRETATION
TREE MODEL
BUILDING
PIPELINE

19. AGGREGATION & FEATURE EXTRACTION
Consumer ID Session Start Time Session End Time Date Price of Purchase
0001 8.00 AM 8.30 AM 01-01-18 0
0002 9.00 AM 10.00 AM 01-02-18 25
0001 5.00 PM 7.00 PM 23-03-18 55
0002 9.00 PM 11.30 PM 05-05-18 50
Consumer ID Total Duration No of Sessions Session Gap Total Price
0001 150 mins 2 81 days 55
0002 210 mins 2 93 days 75
Consumer Profile Rollup

20. Why Random Forest for Behavior Analysis?
● Almost No Input Preparation
● Implicit Feature Selection(Random)
● Very Quick Training
● Tough to Beat!
● Versatility
● Interpretability (Well, based on your efforts!)

21. DEMO Powered By
● Dataset - Google Analytics Consumer Revenue
Prediction
● Model Building
● Feature Analysis
● Partial Dependence
● Tree Interpretation
Jeremy Howard

22. Image Deep Learning in
E-Commerce4

23. Most Valuable 10 mins about Deep Learning...

24. Image Deep Learning Use Cases
Use Case Process Technique
Product Recommendation
Image Similarity Cosine Similarity, NN
Image Popularity Prediction Image Regression
Image Quality Scoring
Image Aestheticity
Calculation
Product Image
Enhancement Image Enhancement Reducing Pixel Loss
Product Category
Classification Image Classification
Transfer Learning/Fine
Tuning

25. Image Deep Learning - Process
● Load, Rescale and Normalize
● Select the CNN Architecture(ResNet, ResNext)
● Retrieve the image embeddings from the final layer
● Perform the operation by adding Fully Connected Layers

26. Image Popularity Prediction
● Click Through Rate Prediction - Amazon, Pinterest
● No of Likes Prediction - Flickr, Facebook etc.
● CTR, Likes - Labels
● Training Deep CNNs (ResNext101) for regression

27. Image Aestheticity Prediction
● Explicit Ranking - Prediction
● Labels - Crowdsourced Ratings for images
● Building a Deep Learning Model to predict the ratings

28. Image Classification
● Product Category Classification - Amazon, eBay
● Product Type (Female Clothing, Backpacks etc.) - Labels
● Training Deep CNNs (ResNext101) for classification

29. Common Image Similarity Measures
◇ Nearest Neighbors
◇ Cosine Similarity
Product Recommendation using Image
Similarity

30. 0.7984 0.7751
0.7110
Similarity Scores using ResNet50

31. 0.8387
Similarity Scores using ResNet50
0.790

32. Data Augmentation
● Augment every single image in different angles
● Increases the effectiveness of prediction
● Model learns more clearly

33. Thanks!
Any questions?
Interested in DL Study group?
You can find me at:
◇ linkedin.com/in/arulbharathi
◇ aarul@sfu.ca