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Deep Neural Networks for YouTube Recommendations

著者: Paul Covington, Jay Adams, Emre Sargin - ACM Conference on Recommender Systems, RecSys 2016 被引用: 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.

An Investigation ofPractical Approximate…An Investigation of Practical Approximate Nearest Neighbor AlgorithmsA Survey ofCollaborative Filtering…A Survey of Collaborative Filtering TechniquesThe YouTube videorecommendation systemThe YouTube video recommendation systemDeep Sparse RectifierNeural NetworksDeep Sparse Rectifier Neural NetworksLarge Scale DistributedDeep NetworksLarge Scale Distributed Deep NetworksDistributedRepresentations of Word…Distributed Representations of Words and Phrases and their CompositionalityDeep content-based musicrecommendationDeep content-based music recommendationPractical Lessons fromPredicting Clicks on Ad…Practical Lessons from Predicting Clicks on Ads at FacebookBatch Normalization:Accelerating Deep…Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftCollaborative DeepLearning for Recommende…Collaborative Deep Learning for Recommender SystemsA Multi-View DeepLearning Approach for…A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation SystemsAutoRec: AutoencodersMeet Collaborative…AutoRec: Autoencoders Meet Collaborative FilteringActiVis: VisualExploration of…ActiVis: Visual Exploration of Industry-Scale Deep Neural Network ModelsAuto-Encoding UserRatings via Knowledge…Auto-Encoding User Ratings via Knowledge Graphs in Recommendation ScenariosFake Co-visitationInjection Attacks to…Fake Co-visitation Injection Attacks to Recommender SystemsTFX: A TensorFlow-BasedProduction-Scale Machin…TFX: A TensorFlow-Based Production-Scale Machine Learning PlatformMulti-Graph ConvolutionCollaborative FilteringMulti-Graph Convolution Collaborative FilteringMGAT: Multimodal GraphAttention Network for…MGAT: Multimodal Graph Attention Network for RecommendationNeural CollaborativeFiltering vs. Matrix…Neural Collaborative Filtering vs. Matrix Factorization RevisitedEscaping the McNamaraFallacy: Towards more…Escaping the McNamara Fallacy: Towards more Impactful Recommender Systems ResearchMachine Learning inBusiness Process…Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome PredictionBRIGHT - Graph NeuralNetworks in Real-Time…BRIGHT - Graph Neural Networks in Real-Time Fraud DetectionWhere to Go Next forRecommender Systems? ID…Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models RevisitedUncovering User Interestfrom Biased and Noised…Uncovering User Interest from Biased and Noised Watch Time in Video RecommendationDeep Neural Networks forYouTube RecommendationsDeep Neural Networks for YouTube Recommendations過去の参考文献中心の論文この論文を引用する論文古い新しい

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