How to Use AI to Personalise Content Recommendations on Your Blog

A reader lands on your blog, scrolls for ten seconds, then leaves. Not because the writing’s bad, but because they can’t see where to go next. Your best post is three clicks away, and they’ll never find it.

That’s where AI-powered content recommendations can help. Done well, it feels like a friendly guide in a bookshop, pointing to the next shelf you’ll enjoy. Done badly, it feels like you’re watching over someone’s shoulder. The goal is the first one: useful, clear, and respectful.

This guide gives you practical steps that work for small blogs, plus privacy-safe tips and a simple path for WordPress or custom sites.

Build the basics first, so AI recommendations feel helpful (not random)

Personalisation starts long before you touch an “AI” button. Think of your blog as a library. If the shelves are messy, even the smartest librarian will hand out strange suggestions.

Your job is to make your content easy to understand, then give the recommendation system a few honest signals to work with. You don’t need a huge dataset, and you don’t need to know someone’s name, job, or postcode. Anonymous behaviour and good content structure can go a long way.

Decide what you will personalise, and what you will not

Before you pick a tool, pick a goal. Personalisation works best when it’s tied to a single reader moment.

Common recommendation goals that actually fit blogs:

• Related posts under an article: “If you liked this, read these next.”
• Next best read on the homepage: a single “Continue reading” card.
• Email ‘what to read next’: links based on what they clicked last time.
• “Because you read…” modules: small, specific prompts that feel human.

Now set boundaries early, while it’s easy to stick to them:

No sensitive guesses: don’t infer health, religion, politics, or finances from behaviour.
No identity tricks: don’t try to “recognise” a person across devices.
No selling personal data: keep recommendation data for your site experience only.

A simple rule that keeps you sane: start with one placement, usually the end of the article. Once it performs well, expand to the homepage, then email.

Get your content and signals ready (topics, metadata, and behaviour)

AI can’t recommend what it can’t understand. Start with the basics:

Clean topics and metadata

• Use consistent categories and tags. Avoid having “AI” and “Artificial Intelligence” as separate tags.
• Write strong titles and excerpts that match what the post delivers.
• Add a short summary to each post (2 or 3 sentences is enough). This becomes perfect input for semantic matching.
• Build simple content clusters. For example, one hub page for SEO, then supporting posts on internal linking, keyword research, and technical audits.

Reader signals you can track (without going creepy)

• Page views (which posts get visited)
• Time on page (did they actually read it)
• Scroll depth (did they reach the end)
• On-site searches (what they tried to find)
• Saves or bookmarks (strong intent)
• Newsletter clicks (what topics pull them back)

There are two ways these signals show up:

Anonymous sessions: most blogs start here. You track behaviour per visit, not per person. It still helps with “related posts” and “popular with readers of this topic”.
Logged-in profiles: useful if you run “My Feed”, “My Saves”, or reading history, but it adds responsibility around consent and data handling.

If you’re a smaller publisher, don’t wait for perfect data. Start with topic structure and lightweight behaviour signals, then improve as your traffic grows.

For ideas on the wider ecosystem of tools (from analytics to personalisation platforms), see AI marketing tool examples and categories.

Choose an AI recommendation approach that matches your blog and budget

“AI recommendations” isn’t one thing. It’s three main approaches, and each has a sweet spot. The right choice depends on how much content you have, how much traffic you get, and how much control you need.

In early 2026, a clear trend is multi-model support (teams switching between GPT, Claude, and Gemini) plus hybrid setups that mix semantic matching with behavioural data. Some sites also add lightweight knowledge graphs so topic relationships get better over time, rather than staying fixed.

Three simple options: rules, machine learning, or LLM-assisted recommendations

A few practical notes:

• Rules-based recommendations can be surprisingly strong if your tagging is tidy.
• Collaborative filtering (the “people who read…” idea) shines once you have enough sessions to avoid weird matches.
• LLM-assisted semantic matching is great for posts that use different wording for the same idea (for example, “personal finance” vs “money habits”).

You’ll see these methods inside common plugin types: related-post engines, SEO plugins that suggest internal links, and chatbot-style “what should I read next?” widgets.

For a grounded overview of how content teams use AI tools without turning everything into automation, this list of AI tools for content creators is a useful reference point.

A small-blog decision checklist (10 minutes to pick your path)

• Platform: WordPress, Ghost, or custom build
• Content volume: under 50 posts, 50 to 300, or 300+
• Traffic level: do you have enough clicks for pattern learning
• Budget per month: free, low, or open-ended
• Need for control: do you want to hand-pick exclusions and topic rules
• Privacy risk level: anonymous sessions only, or logged-in profiles
• Experience style: cards under posts, or a chatbot-style guide

A safe default: start rules-based, add semantic matching once you’ve got clean summaries, and only move to full ML when you have enough behavioural data to trust it.

Set it up, test it, and keep it trustworthy

Personalisation is not “set and forget”. Treat it like a shop display. You move items around, watch what people pick up, then refine.

Many modern tools also offer real-time prediction and built-in testing features, which can be helpful if your blog runs newsletters and multiple channels. Enterprise options exist (some focus on A/B testing and automated targeting), but the process below works even if you’re doing it manually with a simple plugin and analytics.

A practical setup plan you can finish in a weekend

1. Pick one placement: the end of each post is usually best.
2. Choose 6 to 12 starter posts per major topic: your evergreen pieces and your strongest explainers.
3. Create “good matches” manually: add 3 recommended links to each starter post. This acts as seed data and a quality benchmark.
4. Enable AI or semantic matching: feed it your titles, excerpts, and summaries first.
5. Handle the cold start: begin with topic similarity, then blend in behaviour after you have enough clicks.
6. Add feedback: a small “Not for me” option, or “Show me something different”, gives you a clean signal without needing personal data.

If you run a news-style site with fast-moving stories, include at least one “background explainer” in the mix. It stops recommendations becoming a loop of near-identical updates.

Measure what matters, then improve the model and the reader experience

Track outcomes that reflect real reading, not vanity numbers:

• Recommendation click-through rate
• Pages per session
• Return visits within 7 days
• Newsletter sign-ups
• Time to next click (how quickly they find the next useful thing)

Run a simple A/B test for two weeks:

• Variant A: personalised recommendations
• Variant B: a generic “popular posts” module

Then do trust and safety checks:

• Disclose personalisation in plain language (one line is enough).
• Respect cookie consent and offer a non-personalised option.
• Avoid sensitive inferences, even if the model suggests them.
• Rotate fresh content so new posts get a chance.
• Build in diversity: one familiar pick, one adjacent topic, one wild card.

For a broader look at how AI-driven personalisation is being used across marketing channels (and what it means for testing and messaging), this guide to AI in B2C marketing offers helpful context.

Conclusion

AI recommendations don’t need to be mysterious, and they don’t need to be invasive. Set one clear goal, clean up your topics and summaries, pick a lightweight method you can control, then launch in one spot and measure what readers do.

Over time, you’ll learn what your audience wants when they’ve finished a post, and you’ll get better at offering it without guessing who they are. When personalisation is clear, respectful, and useful, it feels like good editing, not surveillance.

Pick one post today, and decide what the next best read should be.