1. Getting Started

• Install Anaconda, review course materials, and create movie recommendations
• Course roadmap
• Understanding you through implicit and explicit ratings
• Top-N recommender architecture
• Review the basics of recommender systems

2. Introduction to Python

• Data structures in Python
• Functions in Python
• Booleans, loops, and a hands-on challenge

3. Evaluating Recommender Systems

• Train/test and cross-validation
• Accuracy metrics (RMSE and MAE)
• Top-N hit rate: Many ways
• Coverage, diversity, and novelty
• Churn, responsiveness, and A/B tests
• Review ways to measure your recommender
• Walkthrough of RecommenderMetrics.py
• Walkthrough of TestMetrics.py
• Measure the performance of SVD recommendations

4. A Recommender Engine Framework

• Our recommender engine architecture
• Recommender engine walkthrough, part 1
• Recommender engine walkthrough, part 2
• Review the results of our algorithm evaluation

5. Content-Based Filtering

• Content-based recommendations and the cosine similarity metric
• K-nearest neighbors (KNN) and content recs
• Producing and evaluating content-based movie recommendations
• Bleeding edge alert: Mise-en-scene recommendations
• Dive deeper into content-based recommendations

6. Neighborhood-Based Collaborative Filtering

• Measuring similarity and sparsity
• Similarity metrics
• User-based collaborative filtering
• User-based collaborative filtering: Hands-on
• Item-based collaborative filtering
• Item-based collaborative filtering: Hands-on
• Tuning collaborative filtering algorithms
• Evaluating collaborative filtering systems offline
• Measure the hit rate of item-based collaborative filtering
• KNN recommenders
• Running user- and item-based KNN on MovieLens
• Experiment with different KNN parameters
• Bleeding edge alert: Translation-based recommendations

7. Matrix Factorization Methods

• Principal component analysis (PCA)
• Singular value decomposition (SVD)
• Running SVD and SVD++ on MovieLens
• Improving on SVD
• Tune the hyperparameters on SVD
• Bleeding edge alert: Sparse linear methods (SLIM)

8. Introduction to Deep Learning

• Deep learning introduction
• Deep learning prerequisites
• History of artificial neural networks
• Playing with TensorFlow
• Training neural networks
• Tuning neural networks
• Introduction to TensorFlow
• Handwriting recognition with TensorFlow, part 1
• Handwriting recognition with TensorFlow, part 2
• Introduction to Keras
• Handwriting recognition with Keras
• Classifier patterns with Keras
• Predict political parties of politicians with Keras
• Intro to convolutional neural networks (CNNs)
• CNN architectures
• Handwriting recognition with CNNs
• Intro to recurrent neural networks (RNNs)
• Training recurrent neural networks
• Sentiment analysis of movie reviews using RNNs and Keras

9. Deep Learning for Recommender Systems

• Intro to deep learning for recommenders
• Restricted Boltzmann machines (RBMs)
• Recommendations with RBMs, part 1
• Recommendations with RBMs, part 2
• Evaluating the RBM recommender
• Tuning restricted Boltzmann machines
• Exercise results: Tuning a RBM recommender
• Auto-encoders for recommendations: Deep learning for recs
• Recommendations with deep neural networks
• Clickstream recommendations with RNNs
• Get GRU4Rec working on your desktop
• Exercise results: GRU4Rec in action
• Bleeding edge alert: Deep factorization machines
• More emerging tech to watch

10. Scaling It Up

• Introduction and installation of Apache Spark
• Apache Spark architecture
• Movie recommendations with Spark, matrix factorization, and ALS
• 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

11. Real-World Challenges of Recommender Systems

• The cold start problem (and solutions)
• Implement random exploration
• Exercise solution: Random exploration
• Stoplists
• Implement a stoplist
• Exercise solution: Implement a stoplist
• Filter bubbles, trust, and outliers
• Identify and eliminate outlier users
• Exercise solution: Outlier removal
• Fraud, the perils of clickstream, and international concerns
• Temporal effects and value-aware recommendations

12. Case Studies

• Case study: YouTube, part 1
• Case study: YouTube, part 2
• Case study: Netflix, part 1
• Case study: Netflix, part 2

13. Hybrid Approaches

• Hybrid recommenders and exercise
• Exercise solution: Hybrid recommenders

Conclusion

• More to explore