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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

Rating system


Each user (when logged in) can rate movies simply by clicking on the rate value (1 to 5) at the side of the movie title in the movies' results page (see preceding screenshot). This action will trigger the rate_movie function in the views.py file (through the corresponding URL in urls.py):

def rate_movie(request):
    data = request.GET
    rate = data.get("vote")
    movies,moviesindxs = zip(*literal_eval(data.get("movies")))
    movie = data.get("movie")
    movieindx = int(data.get("movieindx"))
    #save movie rate
    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))
    
    if MovieRated.objects.filter(movie=movie).filter(user=userprofile...
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