Recommendation System

2023-05-20
ML Applications
  • Airbnb
  • Expedia
  • Facebook News Feed Recommendation
  • LinkedIn
  • Netflix
  • Pinterest
  • Tiktok
  • Tinder
  • Twitter
  • Uber
  • YouTube

Reference

  1. 1.Pinterest:Pinterest Home Feed Unified Lightweight Scoring: A Two-tower Approach ↩
  2. 2.Pinterest:How We Use AutoML, Multi-Task Learning and Multi-Tower Models for Pinterest Ads ↩
  3. 3.Pinterest:SearchSage: Learning Search Query Representation at Pinterest ↩
  4. 4.Google Paper:Towards Disentangling Relevance and Bias in Unbiased Learning to Rank ↩
  5. 5.Google Paper:Revisiting Two-tower Models for Unbiased Learning to Rank ↩
  6. 6.Google Paper:Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendation ↩
  7. 7.Twitter:A SplitNet Architecture for Ad Candidate Ranking ↩
  8. 8.Expedia:Candidate Generation Using a Two Tower Approach with Expedia Group Traveler Data ↩
  9. 9.Video Recommendations at Joyn: Two Tower or Not to Tower, That was Never a Question ↩
  10. 10.LinkedIn:Extracting Skills from Content to Fuel the LinkedIn Skills Graph ↩
  11. 11.Pushing the Limits of the Two-Tower Model ↩
  12. 12.Meta:Scaling the Instagram Explore Recommendation System ↩
  13. 13.Beyond External Embeddings: Integrating User Histories for Enhanced Recommendations ↩
  14. 14.Two-Tower Networks and Negative Sampling in Recommender Systems ↩
  15. 15.Huawei Paper:Position-aware learning to rank ↩
  16. 16.Meituan Paper:A Dual Augmented Two-tower Model for Online Large-scale Recommendation ↩
  17. 17.Uber:Innovative Recommendation Application Using Two Tower Embeddings at Uber ↩
  18. 18.Nvidia:Scale Faster with Less Code Using Two Tower with Merlin ↩
  19. 19.Tinder:Multi-Stage Approach to Building Recommender Systems ↩
  20. 20.Snap:Machine Learning for Snapchat Ad Ranking ↩