1. Introduction
  2. Current Focus
  3. Recommender Systems
    1. Gradient Boosting
    2. TF-IDF
    3. Cross Encoders
    4. SentenceTransformers
    5. Collaborative Filtering
    6. Evaluation
  5. AB Testing
    1. Examples
    2. Power Analysis
  7. LLMs
    1. Fine-tuning
    2. Useful Models
    3. Encoder vs Decoder
    4. Contextualized Recommendations
  9. Miscellaneous
    1. Bradley-Terry Model
    2. Setting up WSL
    3. To Read
    4. Packages
    5. Skills
    6. Hash Collisions
  11. Identities
    1. Sigmoid
    2. Statistics
  13. Papers
    1. Weinberger 2009 - Hashing for Multitask Learning
    2. Rendle 2009 - Bayesian Personalized Ranking
    3. Burges 2010 - RankNET to LambdaMART
    4. Schroff 2015 - FaceNET
    5. Covington 2016 - Deep NNs for Youtube Recs
    6. Schnabel 2016 - Recs as Treatments
    7. Bateni 2017 - Affinity Clustering
    8. Guo 2017 - DeepFM
    9. Hamilton 2017 - GraphSAGE
    10. Ma 2018 - Entire Space Multi-Task Model
    11. Kang 2018 - SASRec
    12. Reimers 2019 - Sentence-BERT
    13. Yi 2019 - LogQ Correction for In Batch Sampling
    14. Zhao 2019 - Recommending What to Watch Next
    15. Lee 2020 - Large Scale Video Representation Learning
    16. He 2020 - LightGCN
    17. Lewis 2020 - Retrieval Augmented Generation
    18. Gao 2021 - SimCSE
    19. Weng 2021 - Contrastive Representation Learning
    20. Dao 2022 - Flash Attention
    21. Zou 2021 - PLM Based Ranking in Baidu Search
    22. Tunstall 2022 - SetFit
    23. Rafailov 2023 - Direct Preference Optimization
    24. Blecher 2023 - Nougat
    25. Dong 2023 - MINE Loss
    26. Liu 2023 - Meaning Representations from Trajectories
    27. Klenitskiy 2023 - BERT4Rec vs SASRec
    28. Singh 2023 - Semantic IDs for Recs
    29. Borisyuk 2024 - GNN at LinkedIn
  15. NLP Course
  16. Database Course
    1. Lecture 1
    2. Lecture 2
    3. Lecture 3