Realtime Contextual Product Recommendations… that scale and generate revenue

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.

Who is this presentation for?
The talk would be of interest to data scientists, data engineers, product managers, UX designers and anyone interested in machine learning.

Prerequisite knowledge:

What you'll learn?

During daytime, Pallav works as a Data Scientist and tries to extract meaningful signals from the noisy world we live in. As the moon rises and evening sets in all bets are off and one might find Pallav on his bike riding through the Berkeley hills in bright colored lycra or performing never-before-scenes of Dramedy with his Improv troupe.  Pallav is a part-time Human Centered Design Thinking coach and has helped non-profits and early-age startups develop clarity on their mission and recognize growth areas. He moved to the Bay Area in 2010 and somehow managed to acquire a Masters in Structural Engineering after spending two years actually learning how to think.  He is an avid follower of Seth Godin, Ken Robinson, and Nicholas Taleb, and is currently looking at ways to explain algorithms through cute, anthropomorphized animals.