Hands-On Recommendation Systems with Python电子书

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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