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Machine Learning for the Web

You're reading from   Machine Learning for the Web Gaining insight and intelligence from the internet with Python

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781785886607
Length 298 pages
Edition 1st Edition
Languages
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Authors (2):
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Andrea Isoni Andrea Isoni
Author Profile Icon Andrea Isoni
Andrea Isoni
Steve Essinger Steve Essinger
Author Profile Icon Steve Essinger
Steve Essinger
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to Practical Machine Learning Using Python FREE CHAPTER 2. Unsupervised Machine Learning 3. Supervised Machine Learning 4. Web Mining Techniques 5. Recommendation Systems 6. Getting Started with Django 7. Movie Recommendation System Web Application 8. Sentiment Analyser Application for Movie Reviews Index

Recommendation systems


This function will use the parameters set at the beginning of the views.py file:

nminimumrates=5
numrecs=5
recmethod = 'loglikelihood'

This defines the minimum number of movies to rate before obtaining recommendations, the number of recommendations to show to the user, and the recommendation system method respectively. To show recommendations the user can click on the Recommendations button on the top bar:

This action will trigger the movies_recs function in the views.py file (through the corresponding URL defined in the urls.py file):

def movies_recs(request):
    
    userprofile = None
    if request.user.is_superuser:
        return render_to_response(
            'books_recsys_app/superusersignin.html', RequestContext(request))
    elif request.user.is_authenticated():
        userprofile = UserProfile.objects.get(user=request.user)
    else:
        return render_to_response(
            'books_recsys_app/pleasesignin.html', RequestContext(request))
    ratedmovies...
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