How to Programmatically Populate Related Blogs in Django: Boosting User Experience and SEO

Originally published at AISaaSTemplate

In this blog post, we will explore a practical solution for Django developers looking to programmatically populate related blogs using Python. This approach is particularly useful for enhancing the functionality of blog platforms or content management systems built with Django.

By implementing this feature, you can significantly improve the user experience on your site by suggesting relevant additional readings to your audience.

We will guide you through setting up your Django environment, computing similarities between blogs, and ultimately updating your database with related blog entries.

Creating a system that programmatically populates related blogs in a Django application is not just about enhancing the user experience; it's also a powerful strategy to improve your site's SEO.

Before we dive into the technical implementation, let's discuss why this feature is a win-win for both your audience and your site's search engine rankings.

The SEO Benefits of Related Blogs

Related blogs contribute significantly to internal linking, a crucial factor in SEO. Internal links are hyperlinks that point from one page to another within the same domain. Here's why they're important for SEO:

1. Navigation: They help users navigate your site more effectively, keeping them engaged longer.
2. Website Hierarchy: Internal links establish a hierarchy on your site, giving Google an idea of the structure of your content and which pages are most important.
3. Link Equity Distribution: They distribute page authority and ranking power throughout your site, improving the SEO of individual pages.

By automatically linking related blogs, you ensure that your site maintains a dynamic internal linking structure. This not only helps Google crawl and index your content more efficiently but also enhances the relevancy of your pages to specific search queries.

The end result? Improved visibility in search engine results pages (SERPs), driving more organic traffic to your site.

Setting Up Your Django Environment

Firstly, ensure your Django project is properly set up and configured. For our demonstration, we're assuming you have a Blog model defined in your Django application, typically located in models.py within your app directory (e.g., your_app_name/models.py). The Blog model should have a ManyToManyField to itself to represent related blogs, which can be defined as follows:

from django.db import models 
class Blog(models.Model): 
    # Other fields (e.g., title, content, etc.) 
    related_blogs = models.ManyToManyField('self', symmetrical=False, related_name='related_to')

It's crucial to have your Django settings properly configured, especially the database settings, since we'll be working directly with the database to populate the related blogs. The code snippet provided at the beginning of this post uses a specific database named 'production' for demonstration purposes. Make sure to adjust this to match your project's configuration.

Computing Similarities Between Blogs

To determine which blogs are related to each other, we employ the Jaccard similarity coefficient, a statistical measure used for gauging the similarity and diversity of sample sets.

We don't want to use common words in deciding the similiarities, so we can exclude them from the following list.

# Set of English stop words
stop_words = ['a',
'about',
'above',
'after',
'again',
'against',
'ain',
'all',
'am',
'an',
'and',
'any',
'are',
'aren',
"aren't",
'as',
'at',
'be',
'because',
'been',
'before',
'being',
'below',
'between',
'both',
'but',
'by',
'can',
'couldn',
"couldn't",
'd',
'did',
'didn',
"didn't",
'do',
'does',
'doesn',
"doesn't",
'doing',
'don',
"don't",
'down',
'during',
'each',
'few',
'for',
'from',
'further',
'had',
'hadn',
"hadn't",
'has',
'hasn',
"hasn't",
'have',
'haven',
"haven't",
'having',
'he',
'her',
'here',
'hers',
'herself',
'him',
'himself',
'his',
'how',
'i',
'if',
'in',
'into',
'is',
'isn',
"isn't",
'it',
"it's",
'its',
'itself',
'just',
'll',
'm',
'ma',
'me',
'mightn',
"mightn't",
'more',
'most',
'mustn',
"mustn't",
'my',
'myself',
'needn',
"needn't",
'no',
'nor',
'not',
'now',
'o',
'of',
'off',
'on',
'once',
'only',
'or',
'other',
'our',
'ours',
'ourselves',
'out',
'over',
'own',
're',
's',
'same',
'shan',
"shan't",
'she',
"she's",
'should',
"should've",
'shouldn',
"shouldn't",
'so',
'some',
'such',
't',
'than',
'that',
"that'll",
'the',
'their',
'theirs',
'them',
'themselves',
'then',
'there',
'these',
'they',
'this',
'those',
'through',
'to',
'too',
'under',
'until',
'up',
've',
'very',
'was',
'wasn',
"wasn't",
'we',
'were',
'weren',
"weren't",
'what',
'when',
'where',
'which',
'while',
'who',
'whom',
'why',
'will',
'with',
'won',
"won't",
'wouldn',
"wouldn't",
'y',
'you',
"you'd",
"you'll",
"you're",
"you've",
'your',
'yours',
'yourself',
'yourselves']

 

The code snippet provided offers a jaccard function to compute this similarity between two sets of words (extracted from blog titles in this case):

 

def jaccard(set1, set2): 
    """Compute the Jaccard similarity of two sets.""" 
    intersection = set1.intersection(set2) 
    union = set1.union(set2) 
    return len(intersection) / len(union) if union else 0

Using this function, we iterate over all combinations of blog titles to compute their pairwise Jaccard similarities. We focus on titles as a simplified example, but you could extend this to other attributes (e.g., content, tags) for a more comprehensive analysis. Here's a simplified version of how we process titles to compute similarities:

 

def compute_jaccard_similarity(blog_titles):
    title_sets = {
        blog_id: set(word for word in title.lower().split() if word not in stop_words)
        for blog_id, title in blog_titles
    }
    similarities = {blog_id: [] for blog_id, title in blog_titles}
    for (id1, title1), (id2, title2) in combinations(blog_titles, 2):
        score = jaccard(title_sets[id1], title_sets[id2])
        similarities[id1].append((id2, score))
        similarities[id2].append((id1, score))
    for blog_id in similarities:
        similarities[blog_id].sort(key=lambda x: x[1], reverse=True)
        similarities[blog_id] = [id for id, score in similarities[blog_id][:3]]
    return similarities

Populating the Database with Related Blogs

After calculating the similarities, we select the top N most similar blogs for each blog entry and update the related_blogs ManyToManyField accordingly. This is where we directly interact with the Django ORM to update the database:

def populate_related_blogs():
    blog_titles = list(Blog.objects.values_list('id', 'title'))
    print("blog_titles: ", blog_titles)

    related_blogs = compute_jaccard_similarity(blog_titles)
    print("related_blogs: ", related_blogs)

    for blog_id, related_ids in related_blogs.items():
        blog = Blog.objects.get(id=blog_id)
        if not blog.related_blogs.count() > 0:
            print("No related blogs found for blog: ", blog.title)
            blog.related_blogs.set(Blog.objects.filter(id__in=related_ids))


# Call this function to populate related blogs
populate_related_blogs()

 

This final step ties everything together by ensuring that each blog entry is linked to its most relevant counterparts in the database, thus enriching the user's browsing experience by suggesting related reads.

One More Thing: Automate Related Blog Populations with Django Command

In our continuous effort to enhance our blog's user experience, we've implemented a feature that ensures you're always in the loop with content that's relevant to your interests. How do we keep things fresh and interconnected, you ask? Let me introduce you to a little behind-the-scenes magic: automated related blog population.

The Power of Automation

Using Django's robust framework, we've crafted a custom Python command that intelligently populates related blogs. This isn't just any script—it's a carefully designed function that links blogs by relevance, ensuring that you're always a click away from exploring similar topics of interest.

Here's a peek under the hood at the Python code making this possible:

from django.core.management.base import BaseCommand
from your_project_slug.blog.utils.populate_related_blogs import populate_related_blogs
from django.utils import timezone

class Command(BaseCommand):
    help = 'Populates related blogs by calling the populate_related_blogs function from blog/utils.'

    def handle(self, *args, **kwargs):
        self.stdout.write(self.style.SUCCESS(f'Starting to populate related blogs at: {timezone.now()}'))
        try:
            populate_related_blogs()
            self.stdout.write(self.style.SUCCESS(f'Successfully populated related blogs at {timezone.now()}'))
        except Exception as e:
            self.stdout.write(self.style.ERROR(f'Error during populating related blogs: {e} at {timezone.now()}'))

You can run Django command line with the following command

python manage.py populate_related_blogs

Scheduling Magic

But, how do we ensure this operation doesn't require manual intervention and the blog universe is always expanding at the right pace? Through the magic of scheduling!

By leveraging Django's management commands, we can schedule this script to run at regular intervals, ensuring that related content is always up to date and dynamically linked.

This scheduled operation runs seamlessly in the background, populating related blogs based on the latest content, without any downtime or disruption to your reading experience.

For example: in Heroku, you can create a new cron add-on and add a daily job to run it at 5am utc.

0 5 * * * /path/to/your/django/project/manage.py populate_related_blogs >> /path/to/your/logfile.log 2>&1

 

Why This Matters

Why go through all this trouble? Because we believe in providing a seamless, engaging, and interconnected reading experience. By automating the process of linking related blogs, we ensure that our content is not just informative but also accessible and relevant, enhancing your journey through our collection of insights, stories, and tutorials.

 

Wrapping Up

Implementing a system to programmatically populate related blogs can significantly enhance the functionality and user experience of your Django-based blog or content management system. By leveraging the power of the Jaccard similarity coefficient and Django's ORM, you can create dynamic, interconnected content that keeps readers engaged and encourages them to explore more of your site's offerings.

Remember, the code snippets provided here are a starting point. You might need to adjust them based on your specific project requirements and database configurations. Additionally, consider expanding the similarity computation beyond just blog titles to include content, tags, or even reader engagement metrics for more sophisticated recommendations.

We hope this guide helps you enhance your Django projects with dynamic related blog functionalities. Happy coding!

Let me know if you're ready to move on or if there's any part of this section you'd like to adjust or expand upon!

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