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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

Models


In this application, we need to store the data related to each movie and the movies' ratings from each user of the website. We set up three models:

class UserProfile(models.Model):
    user = models.ForeignKey(User, unique=True)
    array = jsonfield.JSONField()
    arrayratedmoviesindxs = jsonfield.JSONField()
    lastrecs = jsonfield.JSONField()

    def __unicode__(self):
            return self.user.username

    def save(self, *args, **kwargs):
        create = kwargs.pop('create', None)
        recsvec = kwargs.pop('recsvec', None)
        print 'create:',create
        if create==True:
            super(UserProfile, self).save(*args, **kwargs)
        elif recsvec!=None:
             self.lastrecs = json.dumps(recsvec.tolist())
             super(UserProfile, self).save(*args, **kwargs)
        else:
            nmovies = MovieData.objects.count()
            array = np.zeros(nmovies)
            ratedmovies = self.ratedmovies.all()
            self.arrayratedmoviesindxs = json...
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