From the course: Building Recommender Systems with Machine Learning and AI
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Recommender engine walkthrough, part 1 - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Recommender engine walkthrough, part 1
- [Instructor] At this point you're probably getting a little impatient to start delving into actual recommender algorithms, so I'm not going to spend a ton of time dissecting the code of our recommender system framework, but let's do a quick tour. Start by launching Spyder from your Rexus environment. And open up all of the files inside the Framework folder inside the course materials. Let's work backwards and start from our script that actually uses and tests out this framework. That would be the RecsBakeOff.py file. We start by importing the modules we need. Notably all we need are the recommender algorithms and cells that we want to play with, our MovieLens module that loads our training data, and the Evaluator class we talked about. So the only part of our framework that we really need to understand is the Evaluator module. It hides the complexity of the other modules from us, so we don't have to worry about how our evaluated algorithm or evaluation data modules work. The code…
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Contents
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Train/test and cross-validation3m 49s
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Accuracy metrics (RMSE and MAE)4m 6s
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Top-N hit rate: Many ways4m 35s
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Coverage, diversity, and novelty4m 55s
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Churn, responsiveness, and A/B tests5m 6s
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Review ways to measure your recommender2m 55s
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Walkthrough of RecommenderMetrics.py6m 53s
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Walkthrough of TestMetrics.py5m 8s
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Measure the performance of SVD recommendations2m 24s
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Content-based recommendations and the cosine similarity metric8m 58s
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K-nearest neighbors (KNN) and content recs3m 59s
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Producing and evaluating content-based movie recommendations5m 23s
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Bleeding edge alert: Mise-en-scene recommendations4m 31s
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Dive deeper into content-based recommendations4m 26s
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Measuring similarity and sparsity4m 49s
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Similarity metrics8m 32s
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User-based collaborative filtering7m 25s
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User-based collaborative filtering: Hands-on4m 59s
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Item-based collaborative filtering4m 14s
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Item-based collaborative filtering: Hands-on2m 23s
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Tuning collaborative filtering algorithms3m 31s
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Evaluating collaborative filtering systems offline1m 28s
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Measure the hit rate of item-based collaborative filtering2m 17s
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KNN recommenders4m 4s
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Running user- and item-based KNN on MovieLens2m 26s
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Experiment with different KNN parameters4m 25s
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Bleeding edge alert: Translation-based recommendations2m 29s
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Deep learning introduction1m 30s
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Deep learning prerequisites8m 13s
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History of artificial neural networks10m 51s
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Playing with TensorFlow12m 2s
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Training neural networks5m 47s
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Tuning neural networks3m 52s
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Introduction to TensorFlow11m 29s
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Handwriting recognition with TensorFlow, part 113m 18s
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Handwriting recognition with TensorFlow, part 212m 3s
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Introduction to Keras2m 48s
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Handwriting recognition with Keras9m 52s
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Classifier patterns with Keras3m 58s
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Predict political parties of politicians with Keras9m 55s
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Intro to convolutional neural networks (CNNs)8m 59s
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CNN architectures2m 54s
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Handwriting recognition with CNNs8m 38s
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Intro to recurrent neural networks (RNNs)7m 38s
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Training recurrent neural networks3m 21s
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Sentiment analysis of movie reviews using RNNs and Keras11m 1s
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Intro to deep learning for recommenders2m 19s
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Restricted Boltzmann machines (RBMs)8m 2s
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Recommendations with RBMs, part 112m 46s
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Recommendations with RBMs, part 27m 11s
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Evaluating the RBM recommender3m 44s
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Tuning restricted Boltzmann machines1m 43s
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Exercise results: Tuning a RBM recommender1m 15s
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Auto-encoders for recommendations: Deep learning for recs4m 27s
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Recommendations with deep neural networks7m 23s
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Clickstream recommendations with RNNs7m 23s
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Get GRU4Rec working on your desktop2m 42s
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Exercise results: GRU4Rec in action7m 51s
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Bleeding edge alert: Deep factorization machines5m 49s
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More emerging tech to watch5m 14s
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Introduction and installation of Apache Spark5m 49s
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Apache Spark architecture5m 13s
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Movie recommendations with Spark, matrix factorization, and ALS6m 2s
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Recommendations from 20 million ratings with Spark4m 57s
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Amazon DSSTNE4m 41s
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DSSTNE in action9m 25s
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Scaling up DSSTNE2m 14s
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AWS SageMaker and factorization machines4m 24s
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SageMaker in action: Factorization machines on one million ratings, in the cloud7m 39s
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The cold start problem (and solutions)6m 12s
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Implement random exploration54s
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Exercise solution: Random exploration2m 18s
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Stoplists4m 48s
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Implement a stoplist32s
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Exercise solution: Implement a stoplist2m 22s
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Filter bubbles, trust, and outliers5m 39s
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Identify and eliminate outlier users44s
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Exercise solution: Outlier removal4m
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Fraud, the perils of clickstream, and international concerns4m 33s
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Temporal effects and value-aware recommendations3m 30s
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