(Designing a System to Suggest Relevant Past Content Based on a Reader's Journey)
Let’s be honest: Most blogs (including mine, for a long time) are like a messy library with no librarian. You walk in, look for something, and if you don’t trip over a pile of “related posts” widgets, you probably get lost in the archives or bounce.
But imagine if your blog could act more like a friendly guide—someone who knows what you’ve read, what you might like next, and even what you should probably avoid because it’s too advanced (or too basic).
That’s the dream, right?
Today, I’m diving deep into how you can build a personalized learning path for your readers using machine learning—without needing a team of data scientists or a Google-sized budget. This is the kind of system I wish I’d built years ago, and I’ll share my own experiments, mistakes, and hacks along the way.
Why Bother With Personalization? (And Why Most Blogs Get It Wrong)
If you’ve been blogging for a while, you know the pain:
You write a killer series on, say, “Python for Beginners,” but new readers land on your latest post about “Deep Learning Tricks” and have no clue where to start.
Or, you have dozens of evergreen tutorials, but your “Related Posts” widget just shows whatever is most recent or most popular, not what’s actually relevant to the reader.
Here’s the thing:
Personalization isn’t just a buzzword. It’s what keeps people coming back.
Think about Netflix, YouTube, or even Medium. They’re not just showing you random stuff—they’re building a journey for you, step by step.
But here’s where most blogs mess up:
They show the same “related” posts to everyone, regardless of what the reader has already seen
They don’t account for skill level, topic interest, or learning goals
They make readers do all the work of finding the next useful post
I’ve been guilty of all of the above.
But I wanted better for my readers (and, let’s be honest, for my bounce rate).
What Is a Personalized Learning Path?
Let’s define it simply:
A personalized learning path is a sequence of content recommendations tailored to each reader’s interests, skill level, and previous reading behavior.
It’s like having a smart, lazy assistant who says:
“Hey, since you just finished my ‘Intro to Blogging’ post, you’ll probably love my ‘SEO Basics’ guide next. And if you’re feeling adventurous, here’s a deep dive into ‘Monetizing Your Blog with AdSense.’”
The goal:
Help readers progress from beginner to advanced, at their own pace
Surface hidden gems from your archives
Increase engagement, time-on-site, and hopefully, loyal fans
My Experience: Why I Wanted This (and How I Screwed Up)
A quick story:
A couple of years ago, I noticed that my “How to Start a Blog” post was getting a ton of traffic, but almost nobody was clicking through to my more advanced tutorials.
I tried adding “Next Steps” links manually, but it was a pain to update every time I wrote something new.
Then I tried using Blogger’s built-in “Related Posts” widget. Spoiler: It mostly showed posts from unrelated categories or, worse, posts I’d already linked in the body.
I even tried tagging posts obsessively, hoping the system would figure it out. It didn’t.
That’s when I realized:
If I wanted true personalization, I’d have to build it myself.
But I’m lazy, so I wanted something that could mostly run itself once set up.
The ML Approach: How Machines Can Help (Even If You’re Not a Data Scientist)
Let’s get practical.
Machine learning (ML) sounds intimidating, but for this use-case, you don’t need deep learning or fancy neural networks.
You just need a way to match readers with the most relevant posts, based on what they’ve already read, liked, or interacted with.
At its core, personalization comes down to two things:
Understanding your content (what’s in each post, what level it’s for, what topics it covers)
Understanding your readers (what they’ve read, what they seem to enjoy, how they’re progressing)
If you can connect those two dots, you can suggest the next best post for each person.
Step 1: Mapping Your Content (a.k.a. “What’s in My Blog, Really?”)
Before you can recommend anything, you need to know your content.
This is where most bloggers (including me, for years) get lazy and just rely on tags or categories.
But tags are messy, inconsistent, and often too broad.
Here’s what I did instead:
1.1. Assign Skill Levels
For each post, I assigned a skill level:
Beginner
Intermediate
Advanced
This was a manual process (I just added a custom label in Blogger), but it made a huge difference.
Now, I could avoid recommending “How to Set Up Blogger” to someone who just read “Optimizing TensorFlow Models.”
1.2. Topic Vectors (Don’t Panic, This Is Easy)
I wanted a smarter way to group posts by topic, even if I forgot to tag them.
So, I used a simple NLP trick:
For each post, I extracted the main keywords (using a Python script and the
nltklibrary)I stored these keywords as a “topic vector” for each post
Now, I could calculate similarity between posts based on their keywords, not just their tags.
1.3. Content Metadata
I added a few more metadata fields:
Estimated reading time
Post type (tutorial, opinion, listicle, etc.)
Date published (to avoid recommending outdated stuff)
Lazy tip:
If you don’t want to do this for every post, start with your top 20 most-read articles and expand over time.
Step 2: Understanding Your Readers (Without Creeping Them Out)
Personalization doesn’t mean tracking every move.
But you do need some way to know what a reader has seen.
2.1. Anonymous Tracking
I set up a simple system using cookies (or localStorage if you’re privacy-conscious):
When a reader visits a post, I store the post ID in their browser
Over time, I build up a list of “posts read” for each visitor
No login required, no personal info stored.
It’s not perfect (cleared cookies = lost history), but it’s good enough for most blogs.
2.2. Implicit Feedback
I also track:
Time spent on each post (if they bounce in 5 seconds, maybe they weren’t interested)
Scroll depth (did they read to the end?)
Clicks on “related” or “next” links
This gives me a sense of what they actually engaged with, not just what they landed on.
2.3. Optional: Reader Profiles
If you have a membership or comment system, you can build richer profiles:
Which topics do they comment on?
Which posts do they bookmark or share?
But honestly, most blogs can get by with just anonymous session data.
Step 3: The Recommendation Engine (a.k.a. “What Should I Show Next?”)
This is where the ML magic happens.
There are a few ways to build recommendations, from dead simple to “I-need-a-PhD” complex.
3.1. Rule-Based (The Lazy Way)
If you want something quick:
Recommend the next post in the same skill level and topic
Avoid posts the reader has already seen
This works surprisingly well, especially for linear series (“Part 1, Part 2, …”).
3.2. Content-Based Filtering (Slightly Smarter)
This is what I use:
For each post, calculate similarity (using cosine similarity on the topic vectors)
Recommend the most similar, unread post at the same or next skill level
Example:
If someone just read “How to Use Python Lists (Beginner),” the system might suggest “Python Dictionaries Explained (Beginner)” or “Working with Sets in Python (Intermediate).”
3.3. Collaborative Filtering (If You Have Lots of Readers)
If your blog is big enough (think thousands of readers per month), you can use collaborative filtering:
“Readers who read X also read Y”
This requires more data, but can uncover surprising connections
I haven’t needed this yet, but it’s on my wishlist.
3.4. Hybrid Models (Best of Both Worlds)
Combine content-based and collaborative filtering for even better results.
If you’re feeling ambitious, you can use libraries like Surprise or LightFM in Python.
Step 4: Building the Learning Path (Not Just One-Off Recommendations)
Here’s where it gets interesting.
Instead of just recommending the “next post,” you can build a path—a sequence of posts tailored to the reader’s journey.
4.1. Prerequisite Chains
For technical topics, some posts require knowledge from earlier ones.
I manually mapped out “prerequisite” relationships (e.g., “You should read ‘Intro to Functions’ before ‘Recursion in Python’”)
When a reader finishes a post, I check if they’ve completed the prerequisites for more advanced topics
4.2. Adaptive Paths
If a reader skips ahead to an advanced post and spends a lot of time there, I assume they’re ready for more challenging content.
The system adapts, suggesting higher-level posts
If they bounce quickly, I suggest easier posts or foundational topics.
4.3. Milestones and Achievements
People love a sense of progress.
I added “milestones” (e.g., “You’ve completed the Python Basics track!”)
Optional: Offer badges or certificates (even if they’re just for fun)
This keeps readers motivated and coming back.
Step 5: Displaying Recommendations (Without Being Annoying)
You don’t want to overwhelm readers with popups or endless lists.
5.1. Inline Suggestions
I insert a “Next Recommended Post” box at the end of each article.
Shows the top personalized suggestion
Includes a brief description and estimated reading time
5.2. Sidebar Paths
For logged-in users (or persistent sessions), I show a sidebar with their current learning path:
“You’re on Step 3 of 7 in the Blogging Basics track”
“Up next: How to Monetize Your Blog”
5.3. Gentle Nudges
If a reader returns after a few days, I greet them with:
“Welcome back! Last time you read ‘SEO for Beginners.’ Ready for ‘Advanced SEO Tactics’?”
Not pushy, just helpful.
My Results: What Worked (and What Didn’t)
After rolling out my personalized learning path system, here’s what I noticed:
Wins:
Time-on-site increased by ~30% (people stuck around for more posts)
Bounce rate dropped (fewer people left after reading just one post)
Old posts got new life (hidden gems resurfaced as recommendations)
Reader feedback improved (“I love how your blog guides me step by step!”)
Fails:
Some readers ignored the recommendations entirely (can’t win ‘em all)
My initial skill level assignments were off for a few posts (had to adjust based on feedback)
Tracking broke for users with strict privacy settings (not much you can do)
Hacks for Lazy Bloggers (a.k.a. “How to Do This Without Losing Your Mind”)
Let’s be real: Most of us don’t have time to tag every post or write custom code for every feature.
Here are my shortcuts:
1. Start Small
Pick your top 10 posts and manually map out learning paths.
Expand as you go.
2. Use Automation Tools
If you’re on Blogger, you can’t run Python scripts directly, but you can:
Use Google Sheets to track post metadata
Generate recommendation lists and copy-paste them into your posts
3. Leverage Existing Widgets (But Smarter)
Instead of the default “Related Posts,” create a custom HTML widget that pulls from your own curated list based on skill level and topic.
4. Crowdsource Feedback
Ask readers:
“What did you want to learn next?”
“Was this post too easy, too hard, or just right?”
Use their answers to refine your paths.
5. Don’t Sweat the Perfect Algorithm
Even a basic rule-based system beats random recommendations.
Iterate as you learn what works.
Advanced: Going Full ML (If You’re Feeling Ambitious)
If you want to geek out, here’s how you can take it further:
1. Use Embeddings
Instead of just keywords, use pre-trained language models (like BERT) to generate embeddings for each post.
Calculate similarity between posts using these embeddings for smarter recommendations.
2. Sequence Modeling
Use sequence models (like Markov Chains or even LSTMs) to predict the most likely next post based on a reader’s journey.
3. Real-Time Personalization
If you have a dynamic site (not just Blogger), you can update recommendations in real-time as readers interact.
What Most “Personalization” Advice Gets Wrong
A lot of advice out there assumes you have:
Tons of data
A team of engineers
Readers who log in every time
But most of us have:
A few hundred posts
Readers who are mostly anonymous
Limited time and resources
The trick is to personalize just enough to be helpful, not creepy or overwhelming.
The Unique Perspective: Why Personalized Learning Paths Matter for Your Blog
Here’s my take, after years of trial and error:
Personalized learning paths aren’t just a “nice to have.” They’re the difference between a blog that readers visit once and forget, and a blog that becomes their go-to resource.
For the lazy reader: They don’t have to hunt for the next step. It’s handed to them.
For the overwhelmed beginner: They get a clear roadmap, not a wall of links.
For the advanced user: They skip the basics and jump straight to the good stuff.
And for you, the blogger?
Your best content gets seen, not buried.
You build trust and loyalty, one personalized suggestion at a time.
You get insights into what your audience actually wants, not just what you think they want.
Final Thoughts (And What I’d Do Differently)
If I could go back, I’d start mapping learning paths from day one. Even a simple “If you liked this, read that” is better than nothing.
Machine learning is a tool, not a magic wand.
But even basic personalization can transform your blog from a random collection of posts into a guided journey.
So, what’s stopping you?
Start with your top posts.
Map out a few simple paths.
Add a “Next Recommended Post” box.
Refine as you go.
And if you want to see how I do it, check out some of my other posts:
Have you tried personalizing your blog?
What hacks or tools worked for you?
What would your ideal learning path look like?
Drop a comment or just lurk—either way, I hope this post helps you make your blog a little smarter, a little friendlier, and a lot more useful.
tip: If you only do one thing, add a “Next Step” link to your most popular posts. Your future self (and your readers) will thank you.
Happy blogging, and may your readers always find their way!
0 Comments