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Index
Title Page
Copyright and Credits
Hands-On Recommendation Systems with Python
Dedication
Packt Upsell
Why subscribe?
PacktPub.com
Contributors
About the author
About the reviewer
Image credits
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Code in action
Conventions used
Get in touch
Reviews
Getting Started with Recommender Systems
Technical requirements
What is a recommender system?
The prediction problem
The ranking problem
Types of recommender systems
Collaborative filtering
User-based filtering
Item-based filtering
Shortcomings
Content-based systems
Knowledge-based recommenders
Hybrid recommenders
Summary
Manipulating Data with the Pandas Library
Technical requirements
Setting up the environment
The Pandas library
The Pandas DataFrame
The Pandas Series
Summary
Building an IMDB Top 250 Clone with Pandas
Technical requirements
The simple recommender
The metric
The prerequisties
Calculating the score
Sorting and output
The knowledge-based recommender
Genres
The build_chart function
Summary
Building Content-Based Recommenders
Technical requirements
Exporting the clean DataFrame
Document vectors
CountVectorizer
TF-IDFVectorizer
The cosine similarity score
Plot description-based recommender
Preparing the data
Creating the TF-IDF matrix
Computing the cosine similarity score
Building the recommender function
Metadata-based recommender
Preparing the data
The keywords and credits datasets
Wrangling keywords, cast, and crew
Creating the metadata soup
Generating the recommendations
Suggestions for improvements
Summary
Getting Started with Data Mining Techniques
Problem statement
Similarity measures
Euclidean distance
Pearson correlation
Cosine similarity 
Clustering
k-means clustering
Choosing k
Other clustering algorithms
Dimensionality reduction
Principal component analysis
Other dimensionality reduction techniques
Linear-discriminant analysis
Singular value decomposition
Supervised learning
k-nearest neighbors
Classification
Regression
Support vector machines
Decision trees
Ensembling
Bagging and random forests
Boosting
Evaluation metrics
Accuracy
Root mean square error
Binary classification metrics
Precision
Recall
F1 score
Summary
Building Collaborative Filters
Technical requirements
The framework
The MovieLens dataset
Downloading the dataset
Exploring the data
Training and test data
Evaluation
User-based collaborative filtering
Mean
Weighted mean
User demographics
Item-based collaborative filtering
Model-based approaches
Clustering
Supervised learning and dimensionality reduction
Singular-value decomposition
Summary
Hybrid Recommenders
Technical requirements
Introduction
Case study – Building a hybrid model
Summary
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