In this talk, I will present a broad overview of personalizing recommendations in Online Grocery. Unlike typical recommender systems used in e-commerce that have roots in collaborative filtering, recommending groceries to customers poses unique challenges. How do we handle periodicity in purchases? How do we blend periodicity in purchases with a broad prior based on seasonality? Should recommendations be tailored at an item level, or more broadly at basket level? I will provide a high level end to end overview of online grocery shopping, and as a case study, I will provide deep insights into personalized substitutions. I will describe our state of the art neural network based model that allows order pickers to substitute out of stock items during order fulfilment. Perhaps in a system where we strive to optimize substitutions for our customers, how do we model the inherent bias, akin to a noisy channel, introduced by order pickers? Apart from providing details on personalized substitutions, I will also expand on recent work that touches on basket level substitutions as a multi- objective problem that takes in to account objectives such as cost control and recipe completion.
Kamiya Motwani is a Staff Data Scientist and manager at Walmart Labs India. She is currently a data science lead in Personalization Team. She has also worked extensively on click prediction for advertisements and has rich practical experience building machines that learn from data. Prior to Walmart Labs, she has worked in prestigious organizations such as Oracle corporation and Yahoo Inc. She holds a Master's degree in Computer science from the University of Wisconsin Madison where she focused extensively on Machine learning and probabilistic modelling. Kamiya has also filed several patents in the area of recommender systems, and published papers at premier conferences including NIPS and IEEE ICASSP.