Building Recommender Systems with Machine Learning and AI [Video]
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-
Free ChapterGetting Started
-
Introduction to Python
-
Evaluating a Recommender System
- Train/Test and Cross-Validation
- Accuracy Metrics Using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE)
- Top-N Hit Rate - Many Ways
- Coverage, Diversity, and Novelty
- Churn, Responsiveness, and A/B Tests
- Reviewing Ways to Measure Your Recommender (Quiz)
- Walkthrough of RecommenderMetrics.py (Activity)
- Walkthrough of TestMetrics.py (Activity)
- Measuring the Performance of Singular Value Decomposition (SVD) Recommendations (Activity)
-
A Recommender Engine Framework
-
Content-Based Filtering
- Content-Based Recommendations and the Cosine Similarity Metric
- K-Nearest-Neighbors (KNN) and Content Recommendations
- Producing and Evaluating Content-Based Movie Recommendations (Activity)
- A Note on Using Implicit Ratings
- Bleeding Edge Alert! Mise-En-Scene Recommendations (Activity)
- Diving Deep into Content-Based Recommendations (Exercise)
-
Neighborhood-Based Collaborative Filtering
- Measuring Similarity and Sparsity
- Similarity Metrics
- User-Based Collaborative Filtering
- User-Based Collaborative Filtering - Hands-On (Activity)
- Item-Based Collaborative Filtering
- Item-Based Collaborative Filtering - Hands-On (Activity)
- Tuning Collaborative Filtering Algorithms (Exercise)
- Evaluating Collaborative Filtering Systems Offline (Activity)
- Measuring the Hit Rate of Item-based Collaborative Filtering (Exercise)
- K-Nearest-Neighbor (KNN) Recommenders
- Running User-Based and Item-Based K-Nearest-Neighbor (KNN) on MovieLens Dataset (Activity)
- Experimenting with Different K-Nearest-Neighbor (KNN) Parameters (Exercise)
- Bleeding Edge Alert! Translation-Based Recommendations
-
Matrix Factorization Methods
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- Running Singular Value Decomposition (SVD) and Singular Value Decomposition (SVD ++) on MovieLens (Activity)
- Improving on Singular Value Decomposition (SVD)
- Tuning the Hyperparameters on Singular Value Decomposition (SVD) (Exercise)
- Bleeding-Edge Alert! Sparse Linear Methods (SLIM)
-
Introduction to Deep Learning (Optional)
- Deep Learning Introduction
- Deep Learning Prerequisites
- History of Artificial Neural Networks (ANN)
- (Activity) Playing with TensorFlow
- Training Neural Networks
- Tuning Neural Networks (Avoiding Overfitting)
- Activation Functions: More Depth
- Introduction to TensorFlow
- Handwriting Recognition with TensorFlow – Part 1 (Activity)
- Handwriting Recognition with TensorFlow – Part 2 (Activity)
- Introduction to Keras
- Handwriting Recognition with Keras (Activity)
- Classifier Patterns with Keras
- Predicting Political Parties of Politicians with Keras (Exercise)
- Introduction to Convolutional Neural Network (CNN)
- Convolutional Neural Network (CNN) Architecture
- Handwriting Recognition with Convolutional Neural Network (CNN) (Activity)
- Introduction to Recurrent Neural Networks (RNN)
- Training Recurrent Neural Networks (RNN)
- Sentiment Analysis of Movie Reviews Using Recurrent Neural Networks (RNN) and Keras (Activity)
- Tuning Neural Networks
- Neural Network Regularization Techniques
- Generative Adversarial Networks (GANs)
- GANs in Action
- Generating Images of Clothing with Generative Adversarial Networks (Activity)
-
Deep Learning for Recommender Systems
- Introduction to Deep Learning for Recommenders
- Restricted Boltzmann Machine (RBM)
- Recommendations with Restricted Boltzmann Machine (RBM) – Part 1 (Activity)
- Recommendations with Restricted Boltzmann Machine (RBM) – Part 2 (Activity)
- Evaluating the Restricted Boltzmann Machine (RBM) Recommender (Activity)
- Tuning Restricted Boltzmann Machine (RBM) (Exercise)
- Exercise Results: Tuning a Restricted Boltzmann Machine (RBM) Recommender
- Auto-Encoders for Recommendations: Deep Learning for Recommendation
- Recommendations with Deep Neural Networks (Activity)
- Clickstream Recommendations with Recurrent Neural Networks (RNN)
- Getting GRU4Rec to Work on Your Desktop (Exercise)
- Exercise Results: GRU4Rec in Action
- Bleeding Edge Alert! Generative Adversarial Networks for Recommendations
- TensorFlow Recommenders (TFRS): Introduction and Building a Retrieval Stage
- TensorFlow Recommenders (TFRS): Building a Ranking Stage
- TensorFlow Recommenders (TFRS): Incorporating Side Features and Deep Retrieval
- TensorFlow Recommenders (TFRS): Multi-Task Recommenders, Deep and Cross Networks, ScaNN, and Serving
- Bleeding Edge Alert! Deep Factorization Machines
- More Emerging Tech to Watch
-
Scaling It Up
- Introduction and Installation of Apache Spark (Activity)
- Apache Spark Architecture
- Movie Recommendations with Spark, Matrix Factorization, and Alternating Least Squares (ALS) (Activity)
- Recommendations from 20 Million Ratings with Spark (Activity)
- Amazon Deep Scalable Sparse Tensor Network Engine (DSSTNE)
- Amazon Deep Scalable Sparse Tensor Network Engine (DSSTNE) in Action
- Scaling Up Amazon Deep Scalable Sparse Tensor Network Engine (DSSTNE)
- Amazon Web Services (AWS) SageMaker and Factorization Machines
- Amazon 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
-
Real-World Challenges of Recommender Systems
- The Cold Start Problem (and Solutions)
- Implementing Random Exploration (Exercise)
- Exercise Solution – Random Exploration
- Stoplists
- Implementing a Stoplist (Exercise)
- Exercise Solution – Implementing a Stoplist
- Filtering Bubbles, Trust, and Outliers
- Identifying and Eliminating Outlier Users (Exercise)
- Exercise Solution: Outlier Removal
- Fraud, the Perils of Clickstream, and International Concerns
- Temporal Effects and Value-Aware Recommendations
-
Case Studies
-
Hybrid Approaches
-
Wrapping Up
About this
video
This course will teach you how to use Python, artificial intelligence (AI), machine learning, and deep learning to build a recommender system. From creating a simple recommendation engine to building hybrid ensemble recommenders, you will learn key concepts effectively and in a real-world context.
The course starts with an introduction to the recommender system and Python. Learn how to evaluate recommender systems and explore the architecture of the recommender engine framework. Next, you will learn to understand how content-based recommendations work and get to grips with neighborhood-based collaborative filtering. Moving along, you will learn to grasp model-based methods used in recommendations, such as matrix factorization and Singular Value Decomposition (SVD).
Next, you will learn to apply deep learning, artificial intelligence (AI), and artificial neural networks to recommendations and learn how to scale massive datasets with Apache Spark machine learning. Later, you will encounter real-world challenges of recommender systems and learn how to solve them. Finally, you will study the recommendation system of YouTube and Netflix and find out what a hybrid recommender is.
By the end of this course, you will be able to build real-world recommendation systems that will help users discover new products and content online.
All the resource files are added to the GitHub repository at:
https://github.com/packtpublishing/building-recommender-systems-with-machine-learning-and-ai
- Publication date:
- September 2018
- Publisher
- Packt
- Duration
- 11 hours 24 minutes
- ISBN
- 9781789803273