Deep Neural Networks for YouTube Recommendations
Paul Covington,Jay Adams,Emre Sargin +2 more
- pp 191-198
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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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Journal ArticleDOI
Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube
TL;DR: YouTube still has a long way to go to mitigate misinformation on its platform and a filter bubble effect, both in the Top 5 and Up-Next recommendations for all topics, except vaccine controversies; for these topics, watching videos that promote misinformation leads to more misinformative video recommendations.
Proceedings ArticleDOI
Personalized Key Frame Recommendation
TL;DR: This paper makes use of video images and user time-synchronized comments to design a novel key frame recommender that can simultaneously model visual and textual features in a unified framework and can thus select key frames in a personalized manner, which, to the best of the knowledge, is the first time in the research field of video content analysis.
Proceedings ArticleDOI
Practical Diversified Recommendations on YouTube with Determinantal Point Processes
TL;DR: This work presents a statistical model of diversity based on determinantal point processes (DPPs), and empirical results show that this model, coupled with a re-ranking algorithm, yields substantial short- and long-term increases in user engagement.
Posted ContentDOI
Learning to Represent Human Motives for Goal-directed Web Browsing
TL;DR: GoWeB as discussed by the authors adopts a psychologically-sound taxonomy of higher-ordered goals and learns to build their representations in a structure-preserving manner, then incorporates the resulting representations for enhancing the experiences of common activities people perform on the web.
Journal ArticleDOI
Efficient Neural Matrix Factorization without Sampling for Recommendation
TL;DR: This work derives three new optimization methods through rigorous mathematical reasoning, which can efficiently learn model parameters from the whole data with a rather low time complexity, and presents a general framework named ENMF, short for Efficient Neural Matrix Factorization.
References
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