The document discusses the importance of effective product recommendation systems in enhancing online retail experiences and driving revenue. It highlights key qualities of a good recommender system, such as being intuitive, brand-aligned, customer-focused, and timely in providing relevant information. Additionally, it outlines various recommendation models and the use of data, including consumer preferences and historical purchasing behavior, to refine product suggestions.

1. Realtime Contextual
Product
Recommendations…
… that scale and generate revenue
Pallav Agrawal
Director, Data Science
Levi Strauss & Co.

2. I WANT TO KNOW YOU

6. “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

7. “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!”

8. 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

9. Once Upon a Time...
Why? Based on what
information?
Expensive and discounted
products shown next to each other
random female jean appears

10. Initial Architecture
24h
3rd
Party
API

11. Architecture v2

12. Architecture v3

13. Architecture v4

14. Popularity-Based
Recommendation
Model
source

15. Clearly articulated
source of information
Location based targeting determines
bestselling products in a colder climate

16. Association Rules
Mining based
Recommendation
Model
source

17. Clearly articulated
source of information
Mix of female tees and jeans to
recommend outfitting options

18. Item to Item
Similarity Based
Recommendation
Model
source

19. 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

20. 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

21. Obtaining Image Embeddings

22. Obtaining Image Embeddings

23. Sparse Feature Matrix

24. Curse of Dimensionality

25. Obtaining Image Embeddings

26. Fashion Image
Embeddings

27. [0.01359, 0.00075997, ..., 1.0048, 0.06259]
[-0.24776, -0.12359, 0.20986, ..., 0.079717]
[-0.35609, 0.21854, 0.080944, ..., -0.35413]

28. Transfer Learning with Custom Dataset
BackpropFrozen Weights

29. Document Similarity using Text Embeddings

30. Document Similarity using Text Embeddings

31. High Rise Highrise

32. Whiskers Whiskers

33. Custom Word Embeddings
source

34. Collaborative
Filtering-Based
Recommendation
Model
source

35. Collaborative Filtering

36. Clearly articulated
source of information
Recommended Tees due to past
purchase of Jeans
Women’s tee shown because
of previous mix-gender
product purchases

37. “You have to start with the customer experience and work
backwards to the technology”
Steve Jobs