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# This is a very simple recommender system that recommends posts based on the
# current post user is reading.
import numpy as np
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from .context_processors import add_excerpt, add_num_comments
from .models import Post
def next_read(post):
current_post = Post.objects.get(id=post.id)
posts = Post.objects.filter(is_public=True).exclude(id=current_post.id)
# Our method is very simple. First we compare the bodies of the posts to
# find the similarity between them. Then we sort the posts based on their
# similarity and return the post with the highest similarity.
#
# If no post has similarity > 0.5, we return the post with the highest
# number of views, preferably in the same category. If there is no post in
# the same category, we return the post with the highest number of views
# regardless of the category.
vectorizer = TfidfVectorizer(stop_words='english')
vectors = vectorizer.fit_transform([BeautifulSoup(post.body, 'html.parser').text for post in posts])
current_vector = vectorizer.transform([current_post.body])
similarity = cosine_similarity(current_vector, vectors).flatten()
similarity = np.nan_to_num(similarity)
max_similarity = np.argmax(similarity)
post = posts[int(max_similarity)]
post.excerpt = add_excerpt(post)
post.num_comments = add_num_comments(post)
return post
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