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[Activity] Item-based Collaborative Filtering, Hands-On

  • [Activity] Item-based Collaborative Filtering, Hands-On

[Activity] Item-based Collaborative Filtering, Hands-On

  • November 4, 2019
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Back to: Building Recommender Systems with Machine Learning and AI

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Item-based Collaborative Filtering
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[Exercise] Tuning Collaborative Filtering Algorithms
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Lessons

  • Getting Started
    • [Activity] Install Anaconda, course materials, and create movie recommendations!
    • Course Roadmap
    • What is a Recommender System?
    • Types of Recommenders
    • Understanding You through Implicit and Explicit Ratings
    • Top-N Recommender Architecture
    • [Quiz] Review the basics of recommender systems.
  • Introduction to Python [Optional]
    • [Activity] The Basics of Python
    • Data Structures in Python
    • Functions in Python
    • [Exercise] Booleans, loops, and a hands-on challenge
  • Evaluating Recommender Systems
    • Train/Test and Cross Validation
    • Accuracy Metrics (RMSE, MAE)
    • Top-N Hit Rate – Many Ways
    • Coverage, Diversity, and Novelty
    • Churn, Responsiveness, and A/B Tests
    • [Quiz] Review ways to measure your recommender.
    • [Activity] Walkthrough of RecommenderMetrics.py
    • [Activity] Walkthrough of TestMetrics.py
    • [Activity] Measure the Performance of SVD Recommendations
  • A Recommender Engine Framework
    • Our Recommender Engine Architecture
    • [Activity] Recommender Engine Walkthrough, Part 1
    • [Activity] Recommender Engine Walkthrough, Part 2
    • [Activity] Review the Results of our Algorithm Evaluation.
  • Content-Based Filtering
    • Content-Based Recommendations, and the Cosine Similarity Metric
    • K-Nearest-Neighbors and Content Recs
    • [Activity] Producing and Evaluating Content-Based Movie Recommendations
    • A Note about Implicit Ratings
    • [Activity] Bleeding Edge Alert! Mise en Scene Recommendations
    • [Exercise] Dive Deeper into Content-Based Recommendations
  • Neighborhood-Based Collaborative Filtering
    • Measuring Similarity, and Sparsity
    • Similarity Metrics
    • User-based Collaborative Filtering
    • [Activity] User-based Collaborative Filtering, Hands-On
    • Item-based Collaborative Filtering
    • [Activity] Item-based Collaborative Filtering, Hands-On
    • [Exercise] Tuning Collaborative Filtering Algorithms
    • [Activity] Evaluating Collaborative Filtering Systems Offline
    • [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering
    • KNN Recommenders
    • [Activity] Running User and Item-Based KNN on MovieLens
    • [Exercise] Experiment with different KNN parameters.
    • Bleeding Edge Alert! Translation-Based Recommendations
  • Matrix Factorization Methods
    • Principal Component Analysis (PCA)
    • Singular Value Decomposition
    • [Activity] Running SVD and SVD++ on MovieLens
    • Improving on SVD
    • [Exercise] Tune the hyperparameters on SVD
    • Bleeding Edge Alert! Sparse Linear Methods (SLIM)
  • Introduction to Deep Learning [Optional]
    • Deep Learning Introduction
    • Deep Learning Pre-Requisites
    • History of Artificial Neural Networks
    • [Activity] Playing with Tensorflow
    • Training Neural Networks
    • Tuning Neural Networks
    • Introduction to Tensorflow
    • [Activity] Handwriting Recognition with Tensorflow, part 1
    • [Activity] Handwriting Recognition with Tensorflow, part 2
    • Introduction to Keras
    • [Activity] Handwriting Recognition with Keras
    • Classifier Patterns with Keras
    • [Exercise] Predict Political Parties of Politicians with Keras
    • Intro to Convolutional Neural Networks (CNN’s)
    • CNN Architectures
    • [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs)
    • Intro to Recurrent Neural Networks (RNN’s)
    • Training Recurrent Neural Networks
    • [Activity] Sentiment Analysis of Movie Reviews using RNN’s and Keras
  • Deep Learning for Recommender Systems
    • Intro to Deep Learning for Recommenders
    • Restricted Boltzmann Machines (RBM’s)
    • [Activity] Recommendations with RBM’s, part 1
    • [Activity] Recommendations with RBM’s, part 2
    • [Activity] Evaluating the RBM Recommender
    • [Exercise] Tuning Restricted Boltzmann Machines
    • Exercise Results: Tuning a RBM Recommender
    • Auto-Encoders for Recommendations: Deep Learning for Recs
    • [Activity] Recommendations with Deep Neural Networks
    • Clickstream Recommendations with RNN’s
    • [Exercise] Get GRU4Rec Working on your Desktop
    • Exercise Results: GRU4Rec in Action
    • Tensorflow Recommenders (TFRS): Introduction and Building a Retrieval Stage
    • Tensorflow Recommenders (TFRS): Building a Ranking Stage
    • TFRS: Side Features and Deep Retrieval 
    • TFRS: Deep & Cross Networks, Multi-Task Recommenders, Deploying to Production
    • Bleeding Edge Alert! Deep Factorization Machines
    • More Emerging Tech to Watch
  • Scaling it Up
    • [Activity] Introduction and Installation of Apache Spark
    • Apache Spark Architecture
    • [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS
    • [Activity] 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
    • Other Systems of Note
    • Recommender System Architecture
  • Real-World Challenges of Recommender Systems
    • The Cold Start Problem (and solutions)
    • [Exercise] Implement Random Exploration
    • Exercise Solution: Random Exploration
    • Stoplists
    • [Exercise] Implement a Stoplist
    • Exercise Solution: Implement a Stoplist
    • Filter Bubbles, Trust, and Outliers
    • [Exercise] Identify and Eliminate Outlier Users
    • Exercise Solution: Outlier Removal
    • Fraud, The Perils of Clickstream, and International Concerns
    • Temporal Effects, and Value-Aware Recommendations
  • Case Studies
    • Case Study: YouTube, Part 1
    • Case Study: YouTube, Part 2
    • Case Study: Netflix, Part 1
    • Case Study: Netflix, Part 2
  • Hybrid Approaches
    • Hybrid Recommenders and Exercise
    • Exercise Solution: Hybrid Recommenders
  • Wrapping Up
    • More to Explore
    • Continue your Learning Journey!
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