Trends in Recommendation & Personalization at Netflix

Conference: Transform X 2021

2021-10-07

Authors: Justin Basilico

Summary

Recent trends in improving personalization at Netflix using deep learning, causality, bandits, and objectives

Netflix faces a variety of challenges in personalization, including improving diversity, freshness, and fairness of recommendations
Recent trends in improving personalization include using deep learning for recommendations, causality to understand the impact of recommendations, bandits to optimize recommendations, and objectives to measure success
Deep learning involves learning embeddings for user and item IDs and using them to make predictions
Causality involves understanding the impact of recommendations on user behavior using randomized experiments
Bandits involve optimizing recommendations by balancing exploration and exploitation
Objectives involve measuring success using metrics that capture user satisfaction and fairness
Netflix is hiring for research and summer intern positions in personalization

One challenge in personalization is improving diversity of recommendations. Netflix addresses this by not only recommending popular shows and movies, but also by recommending lesser-known titles that may be of interest to the user. This helps keep recommendations fresh and interesting for users.

Abstract

Justin Basilico, Director of Machine Learning and Recommender Systems at Netflix, where he leads an applied research team that creates algorithms used to personalize the Netflix homepage through machine learning, recommender systems, and large-scale software engineering. In this session, he discusses recent trends in personalization and the challenges of applying machine learning to recommendation systems to create better user experiences.

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