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Realtime Contextual
Product
Recommendations…
… that scale and generate revenue
Pallav Agrawal
Director, Data Science
Levi ...
I WANT TO KNOW YOU
“38% of consumers
say they won’t
return to an online
retailer that
recommends things
that don’t make
sense for them.”
— Ba...
“Tanner was super friendly & helpful from the
moment we walked in the door! He was very
knowledgeable about the different ...
Qualities of a Good
Recommender System
Makes the customer journey intuitive and
frictionless
Best reflects and communicate...
Once Upon a Time...
Why? Based on what
information?
Expensive and discounted
products shown next to each other
random fema...
Initial Architecture
24h
3rd
Party
API
Architecture v2
Architecture v3
Architecture v4
Popularity-Based
Recommendation
Model
source
Clearly articulated
source of information
Location based targeting determines
bestselling products in a colder climate
Association Rules
Mining based
Recommendation
Model
source
Clearly articulated
source of information
Mix of female tees and jeans to
recommend outfitting options
Item to Item
Similarity Based
Recommendation
Model
source
Clearly articulated
source of information
Person browsed girls tees with a prominent Levi’s
logo on a monochrome backgroun...
Product Attributes:
Name: Wedgie Fit Straight Taper Jeans
Gender: Women
Product Type: Jeans
Material: 100% Cotton
Color: L...
Obtaining Image Embeddings
Obtaining Image Embeddings
Sparse Feature Matrix
Curse of Dimensionality
Obtaining Image Embeddings
Fashion Image
Embeddings
[0.01359, 0.00075997, ..., 1.0048, 0.06259]
[-0.24776, -0.12359, 0.20986, ..., 0.079717]
[-0.35609, 0.21854, 0.080944, ......
Transfer Learning with Custom Dataset
BackpropFrozen Weights
Document Similarity using Text Embeddings
Document Similarity using Text Embeddings
High Rise Highrise
Whiskers Whiskers
Custom Word Embeddings
source
Collaborative
Filtering-Based
Recommendation
Model
source
Collaborative Filtering
Clearly articulated
source of information
Recommended Tees due to past
purchase of Jeans
Women’s tee shown because
of prev...
“You have to start with the customer experience and work
backwards to the technology”
Steve Jobs
Realtime Contextual Product Recommendations…that scale and generate revenue - Pallav Agrawal
Realtime Contextual Product Recommendations…that scale and generate revenue - Pallav Agrawal
Realtime Contextual Product Recommendations…that scale and generate revenue - Pallav Agrawal
Realtime Contextual Product Recommendations…that scale and generate revenue - Pallav Agrawal
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Dynamic Talks SF: Recommendation systems are all around us. E-commerce companies like Amazon recommend products we are likely to buy based on our browsing behavior. Netflix suggests what shows we should watch based on our binging habits. Spotify builds a personalized playlist we would enjoy listening to, based on their understanding of what musical genre we are into.

In this talk, we will explore recent advances in the area of product recommendations in both research and practice. We will see how machine learning, design thinking and solid data engineering principles are combined to create an engaging customer experience that positively impacts the bottom line.

We will look at how we use various deep learning architectures to obtain image and text embeddings that supplement user and product based features to generate product recommendations that align closely with a consumer’s aesthetic preferences.

The talk would be of interest to data scientists, data engineers, product managers, UX designers and anyone interested in machine learning.

Realtime Contextual Product Recommendations…that scale and generate revenue - Pallav Agrawal

  1. 1. Realtime Contextual Product Recommendations… … that scale and generate revenue Pallav Agrawal Director, Data Science Levi Strauss & Co.
  2. 2. I WANT TO KNOW YOU
  3. 3. “38% of consumers say they won’t return to an online retailer that recommends things that don’t make sense for them.” — Bazaarvoice Consumers Survey, 2018
  4. 4. “Tanner was super friendly & helpful from the moment we walked in the door! He was very knowledgeable about the different styles and helped me find exactly what I was looking for, even though I wasn’t expecting to find it. He made the experience so much better and I found some amazing pieces! Thank you Tanner!”
  5. 5. Qualities of a Good Recommender System Makes the customer journey intuitive and frictionless Best reflects and communicates your organization’s brand values Focuses on what consumers want, not on who they are Uses both implicit behavioral markers and explicit input Provides relevant information in a timely manner to expedite decision making
  6. 6. Once Upon a Time... Why? Based on what information? Expensive and discounted products shown next to each other random female jean appears
  7. 7. Initial Architecture 24h 3rd Party API
  8. 8. Architecture v2
  9. 9. Architecture v3
  10. 10. Architecture v4
  11. 11. Popularity-Based Recommendation Model source
  12. 12. Clearly articulated source of information Location based targeting determines bestselling products in a colder climate
  13. 13. Association Rules Mining based Recommendation Model source
  14. 14. Clearly articulated source of information Mix of female tees and jeans to recommend outfitting options
  15. 15. Item to Item Similarity Based Recommendation Model source
  16. 16. Clearly articulated source of information Person browsed girls tees with a prominent Levi’s logo on a monochrome background Dark shade tee to create a breakpoint and add visual interest
  17. 17. Product Attributes: Name: Wedgie Fit Straight Taper Jeans Gender: Women Product Type: Jeans Material: 100% Cotton Color: Light Wash Rise: High Rise Size Group: Women’s Regular Fly: Button Fly Fit: Straight, Wedgie Merchant Badge: Waterless Stretch: Non-Stretch Distress: Distressed Price: $110 Leg Opening: Tapered Ankle: Cropped
  18. 18. Obtaining Image Embeddings
  19. 19. Obtaining Image Embeddings
  20. 20. Sparse Feature Matrix
  21. 21. Curse of Dimensionality
  22. 22. Obtaining Image Embeddings
  23. 23. Fashion Image Embeddings
  24. 24. [0.01359, 0.00075997, ..., 1.0048, 0.06259] [-0.24776, -0.12359, 0.20986, ..., 0.079717] [-0.35609, 0.21854, 0.080944, ..., -0.35413]
  25. 25. Transfer Learning with Custom Dataset BackpropFrozen Weights
  26. 26. Document Similarity using Text Embeddings
  27. 27. Document Similarity using Text Embeddings
  28. 28. High Rise Highrise
  29. 29. Whiskers Whiskers
  30. 30. Custom Word Embeddings source
  31. 31. Collaborative Filtering-Based Recommendation Model source
  32. 32. Collaborative Filtering
  33. 33. Clearly articulated source of information Recommended Tees due to past purchase of Jeans Women’s tee shown because of previous mix-gender product purchases
  34. 34. “You have to start with the customer experience and work backwards to the technology” Steve Jobs
  • ChristophBochsler

    Aug. 2, 2019

Dynamic Talks SF: Recommendation systems are all around us. E-commerce companies like Amazon recommend products we are likely to buy based on our browsing behavior. Netflix suggests what shows we should watch based on our binging habits. Spotify builds a personalized playlist we would enjoy listening to, based on their understanding of what musical genre we are into. In this talk, we will explore recent advances in the area of product recommendations in both research and practice. We will see how machine learning, design thinking and solid data engineering principles are combined to create an engaging customer experience that positively impacts the bottom line. We will look at how we use various deep learning architectures to obtain image and text embeddings that supplement user and product based features to generate product recommendations that align closely with a consumer’s aesthetic preferences. The talk would be of interest to data scientists, data engineers, product managers, UX designers and anyone interested in machine learning.

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