3 minute read

  1. Introduction to recommender systems: Basics and classic techniques
  2. Beyond the basics
    1. Ranking
    2. Factorization machines
    3. Explore/exploit
    4. Full page optimization
    5. Context-aware recommendations and other approaches
    6. Reinforcement learning
  3. Deep Learning for recommendations
    1. The Deep Basics
    2. Embeddings
    3. Graph Neural Networks
    4. Recommending Sequences
    5. LLMs for recommendations
  4. The “systems part” of recommender systems
  5. Evaluation and UX
  6. End-to-end examples of real-world industrial recommender systems

1. Introduction to recommender systems: Basics and classic techniques

Introduction to Recommender Systems: A 4-hour lecture [VIDEO]

Data Mining Methods for Recommender Systems

The recommender revolution

On the “Usefulness” of the Netflix Prize

Kdd 2014 Tutorial - the recommender problem revisited

Feature Engineering for Recommendation Systems – Part 1

2. Beyond the basics

2.1 Ranking

What is Learning To Rank?

Personalized ‘Complete the Look’ model by Walmart

Lamdbamart In depth

2.2 Factorization machines

Factorization Machines for Item Recommendation with Implicit Feedback Data

Factorization Machines

2.3 Explore/exploit

Bandits for Recommender Systems

Explore/Exploit Schemes for Web Content Optimization

Explore/Exploit for Personalized Recommendation [VIDEO]

Artwork Personalization at Netflix by Netflix

A Contextual-Bandit Approach to Personalized News Article Recommendation

Recommending Items to Users: An Explore Exploit Perspective

2.4 Full page optimization

Learning a Personalized Homepage by Netflix

Beyond Ranking: Optimizing Whole-Page Presentation [VIDEO]

Fair and Balanced: Learning to Present News Stories

2.5 Context-aware recommendations and other approaches

The Wisdom of the Few: A Collaborative Filtering Approach Based on Expert Opinions from the Web

Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering

Temporal Diversity in Recommender Systems

Towards Time-Dependant Recommendation based on Implicit Feedback

2.6 Reinforcement learning

Reinforcement Learning for Recommendations and Search

Deep Reinforcement Learning for Page-wise Recommendations

3. Deep Learning for recommendations

3.1 The Deep Basics

Neural Collaborative Filtering

Wide & Deep Learning for Recommender Systems by Google

Deep Learning Recommendation Model for Personalization and Recommendation Systems by Facebook

3.2 Embeddings

Embedding-based Retrieval in Facebook Search by Facebook

Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba by Alibaba

3.3 Graph Neural Networks

ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation

Using graph neural networks to recommend related products by Amazon

Modern Recommendation Systems with Neural Networks

Graph Neural Networks in Recommender Systems: A Survey

3.4 Recommending Sequences

Behavior Sequence Transformer for E-commerce Recommendation in Alibaba by Alibaba

Sequential Recommender Systems: Challenges, Progress and Prospects

Recommending movies: retrieval using a sequential model a Tensorflow example

3.5 LLMs for recommendations

Zero and Few Shot Recommender Systems based on Large Language Models

4. The “systems part” of recommender systems

Recommender Systems, Not Just Recommender Models

Real World Recommendation System - Part 1 by Fennel.ai

Blueprints for recommender system architectures: 10th anniversary edition

Pinterest Home Feed Unified Lightweight Scoring: A Two-tower Approach by Pinterest

How NVidia supports Recommender Systems [VIDEO] by NVidia

System Design for Recommendations and Search

Real-time Machine Learning For Recommendations

Near real-time features for near real-time personalization by LinkedIn

Introducing DreamShard: A reinforcement learning approach for embedding table sharding by Facebook

5. Evaluation and UX

The death of the stars: A brief primer on online user ratings

EvalRS: a Rounded Evaluation of Recommender Systems

Beyond NDCG: behavioral testing of recommender systems with RecList

How to Measure and Mitigate Position Bias

Rate it Again: Increasing Recommendation Accuracy by User re-Rating

I like It… I like It Not: Measuring Users Ratings Noise in Recommender Systems

6. End-to-end examples of real-world industrial recommender systems

Lessons Learned from building real life recommender systems

Past, present, and future of recommender systems: An industry perspective [VIDEO]

On YouTube’s recommendation system by Youtube

Deep Neural Networks for YouTube Recommendations by Youtube

How Spotify Uses ML to Create the Future of Personalization by Spotify [VIDEO]

Recommender systems in industry: A Netflix case study by Netflix

Twitter’s recommendation algorithm by Twitter

Evolving the Best Sort for Reddit’s Home Feed by Reddit

Intelligent Customer Preference engine with real-time ML systems by Walmart [VIDEO]

Homepage Recommendation with Exploitation and Exploration by Doordash

Learning to rank restaurants by Swiggy

How we use engagement-based embeddings to improve search and recommendation on Faire by Faire

Deep Recommender Systems at Facebook [VIDEO] by Facebook

Building a heterogeneous social network recommendation system by LinkedIn

A closer look at the AI behind course recommendations on LinkedIn Learning by LinkedIn

Recommending the world’s knowledge. Applications of Recommender Systems at Quora

Monolith: Real Time Recommendation System With Collisionless Embedding Table by Bytedance/Tiktok