Realtime Contextual Product Recommendations…that scale and generate revenue - Pallav Agrawal
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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Realtime Contextual Product Recommendations…that scale and generate revenue - Pallav Agrawal
- 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
- 11. Architecture v2
- 12. Architecture v3
- 13. Architecture v4
- 15. Clearly articulated source of information Location based targeting determines bestselling products in a colder climate
- 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
- 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
- 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
Editor's Notes
- How many of these terms sound familiar to you
- 40% of spotify users use it, Streamed 10s of billion tracks
- 35% of amazon.com revenue comes from it’s recommendations
- Basically everywhere, people expect good recos
- Levi’s store stylists are trained to be really good at helping people who are looking for recos. How do they do that.
- Here;s what makes for a good reco. HMW enable this online?
- Recommendations are effectively a problem of learning how to rank, so we moved to an architecture that would treat recommendations like a search problem. So we turned a trusted search engine backend: ES But the problems were far too many, constantly reindexing the same documents is bad for ES, and recommendations weren’t very relevant. Also, we wanted more flexibility in terms of what products get shown.
- Explaining similarity, NLP embeddings and TF-IDF, along with relevant attribute selection from merch, what’s the data source
- Resnet + added layers, resnet frozen, backprop on new layers
- tf-idf
- tf-idf