Course material for Recommender Systems Udemy course.

• Udemy 101: Getting the Most From This Course
• Note: Alternate dataset download location
• [Activity] Install Anaconda, course materials, and create movie recommendations!
  • 01-GettingStarted/GettingStarted.ipynb
• Course Roadmap
• What Is a Recommender System?
• Types of Recommenders
• Understanding You through Implicit and Explicit Ratings
• Top-N Recommender Architecture
• [Quiz] Review the basics of recommender systems.

• [Activity] The Basics of Python
• Data Structures in Python
• Functions in Python
• [Exercise] Booleans, loops, and a hands-on challenge

• Train/Test and Cross Validation
• Accuracy Metrics (RMSE, MAE)
• Top-N Hit Rate - Many Ways
• Coverage, Diversity, and Novelty
• Churn, Responsiveness, and A/B Tests
• [Quiz] Review ways to measure your recommender.
• [Activity] Walkthrough of RecommenderMetrics.py
• [Activity] Walkthrough of TestMetrics.py
• [Activity] Measure the Performance of SVD Recommendations
  • 03-Evaluating/TestMetrics.ipynb

• Our Recommender Engine Architecture
• [Activity] Recommender Engine Walkthrough, Part 1
• [Activity] Recommender Engine Walkthrough, Part 2
• [Activity] Review the Results of our Algorithm Evaluation.
  • 04-Framework/RecsBakeOff.ipynb

• Content-Based Recommendations, and the Cosine Similarity Metric
• K-Nearest-Neighbors and Content Recs
• [Activity] Producing and Evaluating Content-Based Movie Recommendations
  • 05-ContentBased/ContentRecs.ipynb
• A Note on Using Implicit Ratings.
• [Activity] Bleeding Edge Alert! Mise en Scene Recommendations
• [Exercise] Dive Deeper into Content-Based Recommendations

• Measuring Similarity, and Sparsity
• Similarity Metrics
• User-based Collaborative Filtering
• [Activity] User-based Collaborative Filtering, Hands-On
  • 06-CollaborativeFiltering/SimpleUserCF.ipynb
• Item-based Collaborative Filtering
• [Activity] Item-based Collaborative Filtering, Hands-On
  • 06-CollaborativeFiltering/SimpleItemCF.ipynb
• [Exercise] Tuning Collaborative Filtering Algorithms
• [Activity] Evaluating Collaborative Filtering Systems Offline
  • 06-CollaborativeFiltering/EvaluateUserCF.ipynb
• [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering
• KNN Recommenders
• [Activity] Running User and Item-Based KNN on MovieLens
  • 06-CollaborativeFiltering/KNNBakeOff.ipynb
• [Exercise] Experiment with different KNN parameters.
• Bleeding Edge Alert! Translation-Based Recommendations

• Principal Component Analysis (PCA)
• Singular Value Decomposition
• [Activity] Running SVD and SVD++ on MovieLens
  • 07-MatrixFactorization/SVDBakeOff.ipynb
• Improving on SVD
• [Exercise] Tune the hyperparameters on SVD
  • 07-MatrixFactorization/SVDTuning.ipynb
• Bleeding Edge Alert! Sparse Linear Methods (SLIM)

• Deep Learning Introduction
• Deep Learning Pre-Requisites
• History of Artificial Neural Networks
• [Activity] Playing with Tensorflow
• Training Neural Networks
• Tuning Neural Networks
• Activation Functions: More Depth
• Introduction to Tensorflow
• Important Tensorflow setup note!
• [Activity] Handwriting Recognition with Tensorflow, part 1
• [Activity] Handwriting Recognition with Tensorflow, part 2
• Introduction to Keras
• [Activity] Handwriting Recognition with Keras
• Classifier Patterns with Keras
• [Exercise] Predict Political Parties of Politicians with Keras
• Intro to Convolutional Neural Networks (CNN's)
• CNN Architectures
• [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs)
• Intro to Recurrent Neural Networks (RNN's)
• Training Recurrent Neural Networks
• [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras
• Tuning Neural Networks
• Neural Network Regularization Techniques
• Generative Adversarial Networks (GAN's)
• GAN's in Action
• [Activity] Generating images of clothing with Generative Adversarial Networks

• Intro to Deep Learning for Recommenders
• Restricted Boltzmann Machines (RBM's)
• [Activity] Recommendations with RBM's
  • 09-DeepLearning/RBMBakeOff.ipynb
• [Activity] Evaluating the RBM Recommender
• [Exercise] Tuning Restricted Boltzmann Machines
  • 09-DeepLearning/RBMTuning.ipynb
• Exercise Results: Tuning a RBM Recommender
• Auto-Encoders for Recommendations: Deep Learning for Recs
• [Activity] Recommendations with Deep Neural Networks
• Clickstream Recommendations with RNN's
• [Exercise] Get GRU4Rec Working on your Desktop
• Exercise Results: GRU4Rec in Action
• Bleeding Edge Alert! Generative Adversarial Networks for Recommendations
• Tensorflow Recommenders (TFRS): Intro, and Building a Retrieval Stage
• Tensorflow Recommenders (TFRS): Building a Ranking Stage
• TFRS: Incorporating Side Features and Deep Retrieval
• TFRS: Multi-Task Recommenders, Deep & Cross Networks, ScaNN, and Serving
• Bleeding Edge Alert! Deep Factorization Machines
• Neural Collaborative Filtering (NCF)
• Introducing the LibRecommender Python package
• [Activity] Movie Recommendations with Neural Collaborative Filtering
  • 09-DeepLearning/NeuralCollaborativeFiltering.ipynb
• More Emerging Tech to Watch

• WARNING: Don't install Java 16!
• [Activity] Introduction and Installation of Apache Spark
• Apache Spark Architecture
• [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS
• [Activity] Recommendations from 20 million ratings with Spark
• Amazon DSSTNE
• DSSTNE in Action
• Scaling Up DSSTNE
• AWS SageMaker and Factorization Machines
• SageMaker in Action: Factorization Machines on one million ratings, in the cloud
• Other Systems of Note (Amazon Personalize, RichRelevance, Recombee, and more)
• Recommender System Architecture

• The Cold Start Problem (and solutions)
• [Exercise] Implement Random Exploration
• Exercise Solution: Random Exploration
• Stoplists
• [Exercise] Implement a Stoplist
• Exercise Solution: Implement a Stoplist
• Filter Bubbles, Trust, and Outliers
• [Exercise] Identify and Eliminate Outlier Users
• Exercise Solution: Outlier Removal
• Fraud, The Perils of Clickstream, and International Concerns
• Temporal Effects, and Value-Aware Recommendations

• Case Study: YouTube, Part 1
• Case Study: YouTube, Part 2
• Case Study: Netflix, Part 1
• Case Study: Netflix, Part 2

• Hybrid Recommenders and Exercise
• Exercise Solution: Hybrid Recommenders

• More to Explore
• Bonus Lecture: More courses to explore!