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:
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
Neural Personalized Ranking for Image Recommendation
Wei Niu,James Caverlee,Haokai Lu +2 more
TL;DR: An enhanced model is built by augmenting the basic NPR model with multiple contextual preference clues including user tags, geographic features, and visual factors, which significantly outperforms the base model and a contextual enhanced BPR model in precision and recall.
Proceedings ArticleDOI
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
TL;DR: In this paper, an aspect-aware topic model (ATM) was applied on the review text to model user preferences and item features from different aspects, and estimate the aspect importance of a user towards an item.
Proceedings ArticleDOI
Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach
TL;DR: This work proposes a novel deep fusion model (DFM), which aims to improve the representation learning abilities in deep RSs and can be used for both candidate retrieval and item re-ranking and demonstrates that the proposed DFM is superior to several state-of-the-art models.
Proceedings ArticleDOI
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning
TL;DR: In this article, the authors present a vertically integrated hardware/software co-design, which includes a custom DIMM module enhanced with near-memory processing cores tailored for DL tensor operations.
Proceedings ArticleDOI
Personalized re-ranking for recommendation
Changhua Pei,Yi Zhang,Yongfeng Zhang,Fei Sun,Xiao Lin,Hanxiao Sun,Jian Wu,Peng Jiang,Junfeng Ge,Wenwu Ou,Dan Pei +10 more
TL;DR: The proposed re-ranking model can be easily deployed as a follow-up modular after any ranking algorithm, by directly using the existing ranking feature vectors and directly optimizes the whole recommendation list by employing a transformer structure to efficiently encode the information of all items in the list.
References
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