Hands-On Recommendation Systems with Python

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Book description

With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web

Key Features

• Build industry-standard recommender systems
• Only familiarity with Python is required
• No need to wade through complicated machine learning theory to use this book

Book Description

Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.

This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory - you'll get started with building and learning about recommenders as quickly as possible..

In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques

With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.

What you will learn

• Get to grips with the different kinds of recommender systems
• Master data-wrangling techniques using the pandas library
• Building an IMDB Top 250 Clone
• Build a content based engine to recommend movies based on movie metadata
• Employ data-mining techniques used in building recommenders
• Build industry-standard collaborative filters using powerful algorithms
• Building Hybrid Recommenders that incorporate content based and collaborative fltering

Who this book is for

If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.

Table of contents

1. Title Page
2. Copyright and Credits
   1. Hands-On Recommendation Systems with Python
   1. Why subscribe?
   2. PacktPub.com
   1. About the author
   2. About the reviewer
   3. Image credits
   4. Packt is searching for authors like you
   1. Who this book is for
   2. What this book covers
   3. To get the most out of this book
      1. Download the example code files
      2. Download the color images
      3. Code in action
      4. Conventions used
      1. Reviews
      1. Technical requirements
      2. What is a recommender system?
         1. The prediction problem
         2. The ranking problem
         1. Collaborative filtering
            1. User-based filtering
            2. Item-based filtering
            3. Shortcomings
            1. Technical requirements
            2. Setting up the environment
            3. The Pandas library
            4. The Pandas DataFrame
            5. The Pandas Series
            6. Summary
            1. Technical requirements
            2. The simple recommender
               1. The metric
               2. The prerequisties
               3. Calculating the score
               4. Sorting and output
               1. Genres
               2. The build_chart function
               1. Technical requirements
               2. Exporting the clean DataFrame
               3. Document vectors
                  1. CountVectorizer
                  2. TF-IDFVectorizer
                  1. Preparing the data
                  2. Creating the TF-IDF matrix
                  3. Computing the cosine similarity score
                  4. Building the recommender function
                  1. Preparing the data
                     1. The keywords and credits datasets
                     2. Wrangling keywords, cast, and crew
                     3. Creating the metadata soup
                     1. Problem statement
                     2. Similarity measures
                        1. Euclidean distance
                        2. Pearson correlation
                        3. Cosine similarity
                        1. k-means clustering
                        2. Choosing k
                        3. Other clustering algorithms
                        1. Principal component analysis
                        2. Other dimensionality reduction techniques
                           1. Linear-discriminant analysis
                           2. Singular value decomposition
                           1. k-nearest neighbors
                              1. Classification
                              2. Regression
                              1. Bagging and random forests
                              2. Boosting
                              1. Accuracy
                              2. Root mean square error
                              3. Binary classification metrics
                                 1. Precision
                                 2. Recall
                                 3. F1 score
                                 1. Technical requirements
                                 2. The framework
                                    1. The MovieLens dataset
                                       1. Downloading the dataset
                                       1. Mean
                                       2. Weighted mean
                                       3. User demographics
                                       1. Clustering
                                       2. Supervised learning and dimensionality reduction
                                       3. Singular-value decomposition
                                       1. Technical requirements
                                       2. Introduction
                                       3. Case study – Building a hybrid model
                                       4. Summary
                                       1. Leave a review - let other readers know what you think
                                       Show and hide more

                                       Product information

                                       • Title: Hands-On Recommendation Systems with Python
                                       • Author(s): Rounak Banik
                                       • Release date: July 2018
                                       • Publisher(s): Packt Publishing
                                       • ISBN: 9781788993753

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