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
Proceedings ArticleDOI
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
TL;DR: A novel method based on highly efficient random walks to structure the convolutions and a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model are developed.
Proceedings ArticleDOI
DeepFM: a factorization-machine based neural network for CTR prediction
TL;DR: This paper shows that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions, and combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.
Proceedings ArticleDOI
Deep Interest Network for Click-Through Rate Prediction
Guorui Zhou,Xiaoqiang Zhu,Chenru Song,Ying Fan,Han Zhu,Xiao Ma,Yan Yanghui,Junqi Jin,Han Li,Kun Gai +9 more
TL;DR: A novel model: Deep Interest Network (DIN) is proposed which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad.
Journal ArticleDOI
Deep Learning Based Recommender System: A Survey and New Perspectives
TL;DR: A comprehensive review of recent research efforts on deep learning-based recommender systems is provided in this paper, along with a comprehensive summary of the state-of-the-art.
Posted Content
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
TL;DR: This work proposes a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering, and is much easier to implement and train, exhibiting substantial improvements over Neural Graph Collaborative Filtering (NGCF) under exactly the same experimental setting.
References
More filters
Posted Content
On Using Very Large Target Vocabulary for Neural Machine Translation
TL;DR: It is shown that decoding can be efficiently done even with the model having a very large target vocabulary by selecting only a small subset of the whole target vocabulary.
Proceedings Article
A neural probabilistic model for context based citation recommendation
TL;DR: A novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers is proposed that significantly outperforms other state-of-the-art models in recall, MAP, MRR, and nDCG.
Proceedings Article
User modeling with neural network for review rating prediction
TL;DR: The lexical semantic composition models are extended and a userword composition vector model (UWCVM) is introduced, which effectively captures how user acts as a function affecting the continuous word representation.
Proceedings Article
Label Partitioning For Sublinear Ranking
TL;DR: This work presents a general approach for converting an algorithm which has linear time in the size of the set to a sublinear one via label partitioning, which consists of learning an input partition and a label assignment to each partition of the space such that precision at k is optimized.
Proceedings ArticleDOI
Viral Video Style: A Closer Look at Viral Videos on YouTube
TL;DR: The proposed method is unique in that it is the first attempt to incorporate video metadata into the peak day prediction, and outperforms the state-of-the-art methods, with statistically significant differences.