Deep Neural Networks for YouTube Recommendations
Paul Covington,Jay Adams,Emre Sargin +2 more
- pp 191-198
TLDR
This paper details a deep candidate generation model and then describes a separate deep ranking model and provides practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.Abstract:Â
YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.read more
Citations
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Proceedings ArticleDOI
Latent Cross: Making Use of Context in Recurrent Recommender Systems
TL;DR: This paper offers "Latent Cross," an easy-to-use technique to incorporate contextual data in the RNN by embedding the context feature first and then performing an element-wise product of the context embedding with model's hidden states.
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Graph Neural Networks in Recommender Systems: A Survey
TL;DR: This article provides a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks and systematically analyze the challenges of applying GNN on different types of data.
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Top-K Off-Policy Correction for a REINFORCE Recommender System
TL;DR: This work presents a general recipe of addressing biases in a production top-K recommender system at Youtube, built with a policy-gradient-based algorithm, i.e. REINFORCE, and proposes a noveltop-K off-policy correction to account for the policy recommending multiple items at a time.
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Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
TL;DR: In this paper, the authors proposed a knowledge-aware graph neural network with label smoothness regularization (KGNN-LS) to compute personalized item embeddings by first applying a trainable function that identifies important knowledge graph relationships for a given user and then transform the knowledge graph into a user-specific weighted graph.
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Variational Autoencoders for Collaborative Filtering
TL;DR: In this paper, a variational autoencoder (VAE) was extended to collaborative filtering for implicit feedback, and a generative model with multinomial likelihood and Bayesian inference for parameter estimation was proposed.
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