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Meta is the company behind Facebook, Instagram, and WhatsApp.

Blog posts

  1. Improving Instagram notification management with machine learning and causal inference, 2022-10-31
  2. The new AI-powered feature designed to improve Feed for everyone, 2022-10-05
  3. What is the Instagram Feed?, 2022-02-23
  4. When do recommender systems amplify user preferences? A theoretical framework and mitigation strategies, 2021-08
  5. Shedding More Light on How Instagram Works, 2021-06-08
  6. On the value of diversified recommendations, 2020-12-17
  7. How Instagram suggests new content, 2020-12-10
  8. Designing a Constrained Exploration System, 2020-12-10
  9. Five things I learned about working on content quality at Instagram, 2020-01-25
  10. 🕴 Instagram’s Explore Recommender System, 2019-11-26 HackerNews discussion
    • 3-part funnel (2 layers of candidate generation)
    • domain-specific language (We have separation between model and filter config).
    • account embeddings
    • embedding-based
    • “See fewer posts like this” – explicit feedback
  11. Offline Policy Evaluation: Run fewer, better A/B tests
  12. DLRM: An advanced, open source deep learning recommendation model, 2019-07-02
  13. Lessons Learned at Instagram Stories and Feed Machine Learning, 2018-12-18
  14. Efficient tuning of online systems using Bayesian optimization, 2018-09-17


  1. 🔩🏃 Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models, ISCA 2022 industry track
  2. 🔩🏃 AutoShard: Automated Embedding Table Sharding for Recommender Systems, KDD 2022
  3. 💵🧠 DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction, KDD 2022
  4., JMLR, 2021
  5. Preference Amplification in Recommender Systems, KDD 2021
  6. 🔍 Embedding-based Retrieval in Facebook Search, KDD 2020
  7. Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems, KDD 2020
  8. 👗 Modeling Fashion Influence from Photos, IEEE Transactions on Multimedia, 2020
  9. 🧠 Deep Learning Recommendation Model for Personalization and Recommendation Systems, 2019 (DLRM)
  10. 💵 Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising
  11. Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
  12. 💵 Practical lessons from predicting clicks on ads at facebook, workshop 2014
  13. Supervised Random Walks: Predicting and Recommending Links in Social Networks, WSDM 2011


  1. DLRM recommender: click probability model
  2. Prophet: forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
  3. Facebook AI Performance Evaluation Platform: framework and backend agnostic benchmarking platform to compare machine learning inferencing runtime metrics on a set of models and on variety of backends.
  4. 📑 StarSpace: Learning embeddings for classification, retrieval and ranking.
  5. ReAgent/Horizon: end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator. Tutorial contains e-commerce/recommendation example. paper
  6. faiss: library for efficient similarity search and clustering of dense vectors.
  7. pysparnn: approximate nearest neighbor search for sparse data in Python.
  8. Ax: adaptive experimentation platform,

External links