Authors: Paul Covington , Jay Adams , Emre Sargin - ACM Conference on Recommender Systems, RecSys 2016 cited by 3,364
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.
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An Investigation of Practical Approximate… An Investigation of Practical Approximate Nearest Neighbor Algorithms A Survey of Collaborative Filtering… A Survey of Collaborative Filtering Techniques The YouTube video recommendation system The YouTube video recommendation system Deep Sparse Rectifier Neural Networks Deep Sparse Rectifier Neural Networks Large Scale Distributed Deep Networks Large Scale Distributed Deep Networks Distributed Representations of Word… Distributed Representations of Words and Phrases and their Compositionality Deep content-based music recommendation Deep content-based music recommendation Practical Lessons from Predicting Clicks on Ad… Practical Lessons from Predicting Clicks on Ads at Facebook Batch Normalization: Accelerating Deep… Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Collaborative Deep Learning for Recommende… Collaborative Deep Learning for Recommender Systems A Multi-View Deep Learning Approach for… A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems AutoRec: Autoencoders Meet Collaborative… AutoRec: Autoencoders Meet Collaborative Filtering ActiVis: Visual Exploration of… ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models Auto-Encoding User Ratings via Knowledge… Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios Fake Co-visitation Injection Attacks to… Fake Co-visitation Injection Attacks to Recommender Systems TFX: A TensorFlow-Based Production-Scale Machin… TFX: A TensorFlow-Based Production-Scale Machine Learning Platform Multi-Graph Convolution Collaborative Filtering Multi-Graph Convolution Collaborative Filtering MGAT: Multimodal Graph Attention Network for… MGAT: Multimodal Graph Attention Network for Recommendation Neural Collaborative Filtering vs. Matrix… Neural Collaborative Filtering vs. Matrix Factorization Revisited Escaping the McNamara Fallacy: Towards more… Escaping the McNamara Fallacy: Towards more Impactful Recommender Systems Research Machine Learning in Business Process… Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction BRIGHT - Graph Neural Networks in Real-Time… BRIGHT - Graph Neural Networks in Real-Time Fraud Detection Where to Go Next for Recommender Systems? ID… Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited Uncovering User Interest from Biased and Noised… Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation Deep Neural Networks for YouTube Recommendations Deep Neural Networks for YouTube Recommendations Earlier references Focus paper Citing papers Older Newer Click a node to pin it, click the empty canvas to go back to this paper, or hover to preview. Open a node’s page from its title.
