Deep Neural Networks for YouTube Recommendations
Open accessAbstract
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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AI-PPR can dynamically adjust product recommendations based on users' online behaviors, thereby improving recommendation accuracy and matching.
“AI-PPR can dynamically adjust product recommendations based on users' online behaviors (e.g., likes, viewing duration, sharing), thereby improving recommendation accuracy and matching [30].”
From Exploring the Mechanism of AI-Powered Personalized Product Recommendation on Generation Z Users’ Spontaneous Buying Intention on Short-Form Video Platforms: A Perceived Evaluation Perspective (2025) · cites this paper as [30]
The System Overview and Feature Engineering sections describe personalized YouTube video recommendations that use users’ watch and search histories and respond to recent viewing behavior. The paper does not establish the claim for product recommendations or identify likes and sharing as inputs; its reported improvements concern video-ranking and watch-time metrics, not product-matching accuracy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredWe do not consider non-deep learning approaches for generating item/content embeddings because other works have already proven state-of-the-art performance of deep learning approaches for generating such embeddings.
“We also do not consider non-deep learning approaches for generating item/content embeddings, since other works have already proven state-of-the-art performance of deep learning approaches for generating such embeddings [9, 12, 24].”
From Graph Convolutional Neural Networks for Web-Scale Recommender Systems (2018) · cites this paper as [9]
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- Authors
- Covington, P., Adams, J., & Sargin, E.
- Affiliations
- Google (United States) (US)
- Google, Mountain View, CA, USA
- Venue
- Proceedings of the 10th ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2016-09-07
- Pages
- 191-198
- OpenAlex
- W2512971201
- Cited by
- 3,539
- References
- 26
- Citations by year
- 2026: 346 · 2025: 408 · 2024: 452 · 2023: 466 · 2022: 428 · 2021: 451 · 2020: 433 · 2019: 279 · 2018: 187 · 2017: 86 · 2016: 2 · 2015: 1
- FWCI
- 156.92
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Recommender Systems (RecSys) (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- YouTube, recommender systems, deep neural network, candidate generation, ranking models, video recommendation
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