You're reading from Hands-On Recommendation Systems with Python

Product typeBook

Published inJul 2018

Reading LevelExpert

PublisherPackt

ISBN-139781788993753

Edition1st Edition

Languages

Python

Tools

TensorFlow Scikit-learn

Concepts

Machine Learning

Author (1)

Rounak Banik

Suggestions for improvements

The content-based recommenders we've built in this chapter are, of course, nowhere near the powerful models used in the industry. There is still plenty of scope for improvement. In this section, I will suggest a few ideas for upgrading the recommenders that you've already built:

Experiment with the number of keywords, genres, and cast: In the model that we built, we considered at most three keywords, genres, and actors for our movies. This was, however, an arbitrary decision. It is a good idea to experiment with the number of these features in order to be considered for the metadata soup.
Come up with more well-defined sub-genres: Our model only considered the first three keywords that appeared in the keywords list. There was, however, no justification for doing so. In fact, it is entirely possible that certain keywords appeared in only...

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Hands-On Recommendation Systems with Python

Published in: Jul 2018Publisher: PacktISBN-13: 9781788993753

Author (1)

Rounak 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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