Deep Neural Networks for YouTube Recommendations
Paul Covington,Jay Adams,Emre Sargin +2 more
- pp 191-198
Reads0
Chats0
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:
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
More filters
Posted Content
Personalized Context-aware Re-ranking for E-commerce Recommender Systems.
Changhua Pei,Yi Zhang,Yongfeng Zhang,Fei Sun,Xiao Lin,Hanxiao Sun,Jian Wu,Peng Jiang,Wenwu Ou,Dan Pei +9 more
TL;DR: The proposed re-ranking model directly optimizes the whole recommendation list by employing a transformer structure to encode the information of all items in the list, and introduces the personalized embedding to model the dierences between feature distributions for users.
Posted Content
CryptoRec: Privacy-preserving Recommendation as a Service
TL;DR: This paper proposes CryptoRec, a secure two-party computation protocol for Recommendation as a Service, which encompasses a novel recommender system and possesses two interesting properties: It models user-item interactions in an item-only latent feature space in which personalized user representations are automatically captured by an aggregation of pre-learned item features.
Proceedings ArticleDOI
Lambda Learner: Fast Incremental Learning on Data Streams
Rohan Ramanath,Konstantin Salomatin,Gee Jeffrey Douglas,Kirill Talanine,Onkar Anant Dalal,Gungor Polatkan,Sara Smoot,Deepak Kumar +7 more
TL;DR: This paper proposes Lambda Learner, a new framework for training models by incremental updates in response to mini-batches from data streams, and provides theoretical proof that the incremental learning updates improve the loss-function over a stale batch model.
Journal ArticleDOI
Revisiting Negative Sampling vs. Non-sampling in Implicit Recommendation
TL;DR: The role of negative sampling and non-sampling for implicit recommendation is analyzed, and the results empirically show that although negative sampling has been widely applied to recent recommendation models, it is non-trivial for uniform sampling methods to show comparable performance to non-Sampling learning methods.
Proceedings ArticleDOI
A Dynamic Neural Network Model for Click-Through Rate Prediction in Real-Time Bidding
TL;DR: A dynamic CTR prediction model designed for the Samsung demand-side platform (DSP) and developed using a Dynamic Neural Network model that effectively captures the dynamic evolutions of both users and ads and integrates auxiliary data sources to better model users’ preferences.
References
More filters
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Posted Content
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.
Posted Content
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: In this paper, the Skip-gram model is used to learn high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships and improve both the quality of the vectors and the training speed.