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
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, User-based collaborative filtering
& Item-based collaborative filtering
Tuning collaborative filtering algorithms
Evaluating collaborative filtering systems offline
, Measure the hit rate of item-based collaborative filtering, 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, Tune the hyperparameters on SVD
Handwriting recognition with TensorFlow & Keras,, Classifier patterns with Keras, Predict political parties of politicians with Keras, Intro to convolutional neural networks (CNNs), Handwriting recognition with CNNs, Intro to recurrent neural networks (RNNs)
Training recurrent neural networks
Sentiment analysis of movie reviews using RNNs and Keras, Recommendations with RBMs
Tuning restricted Boltzmann machines
Auto-encoders for recommendations: Deep learning for recs, Recommendations with deep neural networks, Clickstream recommendations with RNNs
Get GRU4Rec working on your desktop,
Bleeding edge alert: Deep factorization machines
Introduction and installation of Apache Spark
Movie recommendations with Spark, matrix factorization, and ALS, Recommendations from 20 million ratings with Spark, Amazon DSSTNE
, Scaling up DSSTNE
AWS SageMaker and factorization machines
Factorization machines on one million ratings, in the cloud, The cold start problem (and solutions)
Implement random exploration,Stoplists
, Filter bubbles, trust, and outliers
Fraud, the perils of clickstream, and international concerns, Temporal effects and value-aware recommendations