🙌 Recommendation Systems

A one-stop shop for all things RecSys!.
📌 Table of Contents
overview; retrieval; ranking
RecSys Toolkit
embedding space; alternating least squares; matrix factorization; mean normalization
Candidate Generation
notation; content-based filtering; code deep-dive; collaborative filtering; code deep-dive; retrieval
Ranking / Scoring
overview; scoring with the candidate generator?; objective function for scoring; positional bias
overview; freshness; diversity; fairness
Building a Music Recommendation System using PySpark
code deep-dive; data preprocessing; data aggregation; data split; spin up your model!
Evaluation, Metrics, Testing, and Loss function for Recommender Systems
evaluating candidate generation; evaluating scoring; evaluating ranking; A/B testing
Content Moderation for Recommender Systems
videos, text, and image moderation; early and late moderation
Summary of relevant papers
popular recommendation systems in production demystified
Position Bias
reasons for position bias; measuring positional bias; mitigation strategies
Cold Start Problem
types of cold start; mitigation strategies
Multi-armed bandit
exploration; exploitation; contextual bandit
📖 References
📝 Citation
If you found our work useful, please cite it as:
  author        = {Jain, Vinija},
  title         = {Notes for Recommender Systems},
  howpublished  = {\url{https://www.vinija.ai}},
  year          = {2022},
  note          = {Accessed: 2022-07-01},
  url           = {www.vinija.ai}

V. Jain, Notes for Recommendation Systems https://www.vinija.ai, 2022, Accessed: July 1 2022.