Track

Recommender Systems

Goal: Learn ranking and retrieval for personalized recommendations, from classical collaborative filtering through matrix factorization, deep CTR models, two-stage industrial systems, and sequential recommenders.

Prereqs: ML Basics. Deep Learning helps for the neural ranking and retrieval papers.

Status: done

Work through the steps in order. Bold links open YouTube.

Step	Concept	YouTube	Read
1	Recommendation problem and candidate stages	The Math Behind Recommender Systems	Google Developers — Recommendation systems overview
2	Content-based filtering	Content-Based Recommendations	Google Developers — Content-based filtering
3	Collaborative filtering	Recommendation Systems — Collaborative Filtering	Google Developers — Collaborative filtering
4	Item-based neighborhood methods		Sarwar et al. 2001 — Item-Based Collaborative Filtering Recommendation Algorithms
5	Matrix factorization	How does Netflix recommend movies? Matrix Factorization	Koren, Bell & Volinsky 2009 — Matrix Factorization Techniques for Recommender Systems
6	Implicit feedback and BPR	BPR: Bayesian Personalized Ranking from Implicit Feedback	Rendle et al. 2009 — BPR: Bayesian Personalized Ranking from Implicit Feedback
7	Wide & Deep memorization and generalization	Wide & Deep Learning for Recommender Systems	Cheng et al. 2016 — Wide & Deep Learning for Recommender Systems
8	Neural collaborative filtering	Neural Collaborative Filtering (NCF)	He et al. 2017 — Neural Collaborative Filtering
9	DeepFM for CTR ranking	DeepFM for recommendation systems explained	Guo et al. 2017 — DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
10	Two-stage candidate generation and ranking	Deep Neural Networks for YouTube Recommendations	Covington, Adams & Sargin 2016 — Deep Neural Networks for YouTube Recommendations
11	Two-tower retrieval	Building Scalable Retrieval System with Two-Tower Models
12	Self-attentive sequential recommendation	Self-Attentive Sequential Recommendation (SASRec)	Kang & McAuley 2018 — Self-Attentive Sequential Recommendation

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