Building Recommender Systems With Machine Learning and AI · Help People Discover New Products and Content With Deep Learning, Neural Networks, and Machine Learning Recommendations. by Kane, Frank -- Read -- Imperial Library of Trantor

Index

Getting Started

Introduction Getting Set Up Course Overview

Overview of Recommender Systems

Applications of Recommender Systems Gathering Interest Data Top-N Recommenders Quiz

Introduction to Python Evaluating Recommender Systems

Testing Methodologies Accuracy Measures Hit Rate Measures Coverage Diversity Novelty Churn Responsiveness A/B Tests Quiz Measuring Recommenders with Python

Recommender Engine Design Content-Based Filtering

Attribute-based Recommendations Cosine Similarity K-Nearest Neighbors Coding Activity Bleeding Edge Alert! Mise en Scène Similarities Coding Exercise

Neighborhood-Based Collaborative Filtering

Top-N Architectures Cosine Similarity Sparsity Adjusted Cosine Pearson Similarity Spearman Rank Correlation Mean Squared Difference Jaccard Similarity User-based Collaborative Filtering Coding Activity Item-Based Collaborative Filtering Coding Activity KNN Recommenders Coding Activity Bleeding Edge Alert! Translation-Based Recommendations

Model-Based Methods

Matrix Factorization Principal Component Analysis Coding Activity: SVD Flavors of Matrix Factorization Coding Exercise Bleeding Edge Alert! Sparse Linear Methods

Recommendations with Deep Learning

Introduction to Deep Learning

Deep Learning Pre-requisites Artificial Neural Networks Deep Learning Networks Using TensorFlow Using Keras Convolutional Neural Networks Recurrent Neural Networks

Recommendations with Deep Learning

Restricted Boltzmann Machines Coding Exercise Deep Neural Networks for Recommendations Autoencoders Coding Activity Using RNN’s for Session-Based Recommendations Coding Exercise Bleeding Edge Alert! Deep Factorization Machines Word2Vec 3D CNN’s

Scaling it Up

Apache Spark and MLLib Coding Activity Amazon DSSTNE Coding Activity AWS SageMaker

Challenges of Recommender Systems

The Cold-Start Problem Exercise: Random Exploration Stoplists Filter Bubbles Trust Outliers and Data Cleaning Malicious User Behavior The Trouble with Click Data International Considerations The Effects of Time Optimizing for Profit

Case Studies

YouTube

Learning to Rank

Netflix

Hybrid Recommenders

Coding Exercise

More to Explore

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