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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Dynamic Set kNN Self-Join
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Learning to Index for Nearest Neighbor Search
TL;DR: This study presents a novel ranking model based on learning neighborhood relationships embedded in the index space that can replace the conventional distance-based ranking for finding candidate clusters and the predicted probability can be used to determine the data quantity to be retrieved from the candidate cluster.
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"How over is it?" Understanding the Incel Community on YouTube
Kostantinos Papadamou,Savvas Zannettou,Jeremy Blackburn,Emiliano De Cristofaro,Gianluca Stringhini,Michael Sirivianos +5 more
TL;DR: In this article, the authors analyze the Incel community on YouTube by focusing on this community's evolution over the last decade and understand whether YouTube's recommendation algorithm steers users towards Incel-related videos.
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An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem
Mahamudul Hasan,Falguni Roy +1 more
TL;DR: Two item-based similarity measures have been designed to overcome the problem of cold-start problem and an enhanced prediction algorithm has been proposed so that it can calculate a better prediction for the recommendation.
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Hashtag our stories: Hashtag recommendation for micro-videos via harnessing multiple modalities
TL;DR: A neural network-based solution, LOGO (short for “muLti-mOdal-based hashtaG recOmmendation”), to recommend hashtags for micro-videos by utilizing multiple modalities using a multi-view representation learning framework.
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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