Title: Machine Learning@Amazon
Speaker: Rajeev Rastogi, Amazon
In this talk, I will first provide an overview of the key Machine
Learning (ML) applications we are developing at Amazon. I will then
describe a matrix factorization model that we have developed for making
product recommendations - the salient characteristics of the model are:
(1) It uses a Bayesian approach to handle data sparsity, (2) It
leverages user and item features to handle the cold start problem, and
(3) It introduces latent variables to handle multiple personas
associated with a user account (e.g. family members). Our experimental
results with synthetic and real-life datasets show that leveraging user
and item features, and incorporating user personas enables our model to
provide lower RMSE and perplexity compared to baselines.
Rajeev is the director of the Machine Learning Group at Amazon.