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Hands-On Recommendation Systems with Python

You're reading from Hands-On Recommendation Systems with Python Start building powerful and personalized, recommendation engines with Python

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781788993753

Length 146 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Banik

View More author details

Table of Contents (15) Chapters

Title Page

Copyright and Credits

Dedication

Packt Upsell

Contributors

Preface

1. Getting Started with Recommender Systems FREE CHAPTER

2. Manipulating Data with the Pandas Library

3. Building an IMDB Top 250 Clone with Pandas

4. Building Content-Based Recommenders

5. Getting Started with Data Mining Techniques

6. Building Collaborative Filters

7. Hybrid Recommenders

1. Other Books You May Enjoy

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Index

The knowledge-based recommender

In this section, we are going to go ahead and build a knowledge-based recommender on top of our IMDB Top 250 clone. This will be a simple function that will perform the following tasks:

Ask the user for the genres of movies he/she is looking for
Ask the user for the duration
Ask the user for the timeline of the movies recommended
Using the information collected, recommend movies to the user that have a high weighted rating (according to the IMDB formula) and that satisfy the preceding conditions

The data that we have has information on the duration, genres, and timelines, but it isn't currently in a form that is directly usable. In other words, our data needs to be wrangled before it can be put to use to build this recommender.

In our Chapter3 folder, let's create a new Jupyter Notebook named Knowledge Recommender. This notebook will contain all the code that we write as part of this section.

As usual, let us load our packages and the data into our notebook. Let...

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Authors (1)

Banik

Banik

Rounak Banik is a Young India Fellow and an ECE graduate from IIT Roorkee. He has worked as a software engineer at Parceed, a New York start-up, and Springboard, an EdTech start-up based in San Francisco and Bangalore. He has also served as a backend development instructor at Acadview, teaching Python and Django to around 35 college students from Delhi and Dehradun. He is an alumni of Springboard's data science career track. He has given talks at the SciPy India Conference and published popular tutorials on Kaggle and DataCamp.

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