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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Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders
TL;DR: A co-training disentangled domain adaptation network (CD$^2$AN), which can co-train both biased and unbiased models and outperforms the existing debiased solutions on popularity distribution shift and long-tail distribution shift.
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A personalized recommendation system for multi-modal transportation systems
Fanyou Wu,Cheng-Rui Lyu,Yang Liu +2 more
TL;DR: Wang et al. as discussed by the authors proposed a conceptual framework for proactive travel mode recommendation combining recommendation system and transportation engineering, which works by learning from historical user behavioral preferences and ranking the candidate travel modes.
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Building Effective Short Video Recommendation
TL;DR: This paper focuses on constructing a universal framework for short video recommendation by predicting the probability of finishing watching the entire video and pressing the 'like' button, and four novel techniques are proposed to improve the prediction accuracy.
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Linkless Link Prediction via Relational Distillation
TL;DR: This work proposes a relational KD framework, Linkless Link Prediction (LLP), which boosts the link prediction performance of MLPs with significant margins, and even outperforms the teacher GNNs on 6 out of 9 benchmarks.
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KEAN: Knowledge Embedded and Attention-based Network for POI Recommendation
TL;DR: A deep learning framework KEAN (Knowledge Embedded and Attention Based Network) based on knowledge graph and attention model is proposed, which uses fully-connected neural networks to realize recommendations in POI recommendation.
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