Exercise solution: Hybrid recommenders

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

Data structures in Python

Functions in Python

Booleans, loops, and a hands-on challenge

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

Our recommender engine architecture

Recommender engine walkthrough, part 1

Recommender engine walkthrough, part 2

Review the results of our algorithm evaluation

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

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

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)

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

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

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

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

Case study: YouTube, part 1

Case study: YouTube, part 2

Case study: Netflix, part 1

Case study: Netflix, part 2

Hybrid recommenders and exercise

More to explore