Listen to this post: Personalising Website Content with AI Recommendations (Without Creeping People Out)
Two readers land on the same homepage at the same time.
One is a founder on a train, thumb-scrolling for funding and markets. The other is a student at a kitchen table, hunting for a clean explainer on today’s big political story. Same site, same minute, two very different needs. Yet most homepages treat them like twins.
That’s where AI recommendations earn their keep. In plain terms, the site learns what a reader seems to care about (from what they click, save, search, or skip) and then suggests the next most useful piece of content. It’s less “mind-reading”, more “good shop assistant who remembers what you asked for last time”.
On an AI-curated news platform like CurratedBrief, this can shape how Top Stories, My Feed, My Interests, and My Saves feel day-to-day. Done well, it changes the ordering and discovery of stories, not the truth of the reporting. It can also tune prompts (follow a topic, sign up to a newsletter, save for later) so they fit the moment, not just the marketing plan.
What AI recommendations really do on a website (and what they don’t)
AI recommendations are best understood as matchmaking. The system looks at signals, then tries to pair a reader with the next right story, topic, or action.
Think “Netflix suggestions”, but for:
- articles and explainers
- topic collections (AI, markets, health, geopolitics)
- actions like “save this”, “follow this topic”, or “get the morning email”
What they don’t do is magically create interest where none exists. If the content is thin, personalisation won’t fix it. Also, recommendations should not change the facts a reader sees. For a news site, the goal is safer: help people find what matters to them faster, while keeping a shared reality.
When it’s working, readers feel three things:
- Faster discovery (less hunting, more reading)
- Longer sessions (because the next suggestion makes sense)
- More return visits and sign-ups (because the site starts to feel “for me”)
If you want a broader view of how teams implement this, HubSpot’s overview of AI content personalisation gives a useful, practical framing.
The simple building blocks: signals in, decisions in the middle, personalised content out
Personalisation sounds fancy until you break it into three parts.
1) Signals in (what the site can observe or ask for)
Common, low-drama signals include:
- clicks and taps
- reading time (with care, it’s noisy)
- scroll depth
- searches
- saves and follows
- device type (mobile readers behave differently)
- time of day (morning skim vs late-night deep read)
Location can be helpful, but only when it clearly improves relevance (local elections, transport strikes, region-specific markets). Otherwise it risks feeling like a guess.
2) Decisions in the middle (how the system picks)
This “brain” can be a machine-learning model, a rules engine, or a blend. Early on, rules often win because they’re easier to audit. As data grows, models help find patterns you’d miss manually.
3) Personalised content out (what the reader actually sees)
Typical outputs include:
- “Recommended for you” cards
- topic rails (AI, finance, sport) ordered by interest
- smarter search suggestions
- “continue reading” prompts based on past behaviour
A concrete example: a reader saves three finance explainers and follows “markets”. On their next visit, the homepage still shows the day’s must-know headlines, but the second rail shifts towards market context pieces, earnings explainers, and “what it means” summaries. The site isn’t guessing their salary or job title. It’s responding to what they did.
Common myths that lead to bad personalisation
Bad personalisation usually comes from one of four myths.
Myth 1: “Copy what big retailers do.”
Retailers often have huge purchase datasets. A news site may not. If you force a complex system onto thin signals, it gets jumpy and wrong.
Myth 2: “Personalisation will cover weak content.”
If articles don’t answer real questions, the best algorithm just recommends disappointment faster.
Myth 3: “More personal is always better.”
Over-targeting can feel invasive. Personalisation should feel like a better menu, not surveillance.
Myth 4: “It’s fine if different readers see different facts.”
For news, that’s a red line. Set editorial guardrails: the reporting stays the same, the ordering and path through it can change.
Where to personalise: high-impact spots that boost reads, saves, and sign-ups
Personalisation works best when it supports the reader’s goal. On a content site, that goal is often simple: understand something quickly, follow a topic over time, or save it for later.
In January 2026, the big trend is hyper-personalisation, meaning the site adjusts in near real time while someone browses, not just between visits. If a reader keeps opening explainers instead of breaking news, the site can shift the next set of suggestions within the same session.
If you’re collecting ideas for placements and patterns, Fresh Relevance’s guide to website personalisation strategies and best practices is a solid reference point, even if your end goal is editorial rather than ecommerce.
Homepage and section pages: smarter Top Stories without losing the big picture
The homepage is a promise. If it becomes too personalised, it can stop feeling like “today’s news”. If it’s not personalised at all, it becomes a wall.
A balanced approach:
- Personalised ordering, not fully personalised selection (at least at first)
- Topic tiles that reflect what a reader follows (AI, markets, health)
- Local or sector angles only when they’re clearly relevant
- A mix rule (for example, 70 percent personal relevance, 30 percent broad importance)
A practical safety feature is a fixed “must-know” strip. It protects against filter bubbles and keeps a shared front page, while still letting the rest of the page feel tailored.
Article pages: next-best reads, better CTAs, and helpful context panels
Article pages are where intent becomes clearer. The reader has chosen something, which is a strong signal.
High-value personalisation here includes:
Related story recommendations that respect context
If someone reads a market drop story, suggest the explainer on why bonds moved, not a random trending celebrity piece.
“Catch-up in 3 links” panels
This works brilliantly for ongoing stories. The system can pick the three most useful background pieces based on what the reader has already read.
“What this means for you” cards by reader type
You don’t need to know someone’s identity. You can offer a simple choice: investor, founder, student, or just “keep it general”. The content stays accurate; the framing changes.
Personalised CTAs that fit the moment
A new reader might see “Get the daily brief”. A logged-in reader who saves often might see “Save this for later” or “Follow this topic”.
Interactive media also matters more in 2026. Short clips, charts, and mini explainers can adapt to what someone engages with. If they watch short video summaries, show more of them. If they hover charts and scroll slowly, prioritise data-led context.
For a more marketing-led angle on how teams operationalise this, SAP Emarsys’ AI-powered website personalisation guide offers a useful look at how recommendation logic connects to sign-ups and loyalty.
My Feed, My Interests, My Saves: personalisation that readers can see and control
The most trusted personalisation is the kind readers can see and steer.
When a reader opens “My Interests” and ticks AI, business, and health, they understand why their feed changes. When they can remove a topic in one tap, the system feels polite.
Tactics that build trust without slowing the product down:
- Interest selection on onboarding (keep it short, let them skip)
- Easy topic toggles (on and off, no buried settings)
- “Why am I seeing this?” labels on recommendations
- Simple feedback buttons, like “more like this” and “less like this”
This kind of control supports retention in a quiet way. It helps readers build a habit, because the site starts to feel predictable in the best sense: it remembers what they asked for.
How to build AI recommendations without breaking trust (data, privacy, and fairness)
Personalisation is a value exchange. Readers give signals; you give a better experience. If you take too much and give too little, people leave.
In 2026, personalisation also travels across channels. A reader’s web behaviour might shape email picks, and app alerts might reflect what they save on desktop. That can be helpful, but only if consent and preferences travel too.
Start with low-risk data: first-party signals and clear consent
Start with what readers already do on your site. It’s simpler, safer, and usually enough to get early wins.
Good starting signals:
- on-site reading and clicks
- saves, follows, and history
- newsletter preferences (topics, frequency)
- searches and filters
Keep consent language plain. If personalisation uses cookies beyond what’s needed for basic site function, give a real choice. In some cases, personalisation should be off by default until the reader opts in, especially if you use cross-site data.
Also set a retention window. Don’t keep behavioural data forever “just in case”. Many teams choose rolling windows (for example, 30 to 90 days) so the feed reflects current interests, not last summer’s obsession.
Avoid the creepy line: transparency, controls, and sensible limits
Creepy personalisation often sounds like it knows too much. It can also feel creepy when it’s too confident.
What tends to trigger that reaction:
- sudden use of sensitive topics (health, grief, religion) without a clear ask
- hyper-local guesses that feel intrusive
- overly personal wording (“We know you’re worried about…”)
Simple rules that keep it human:
- show a brief reason for a recommendation (“because you saved…”)
- let people reset their profile
- cap repetition (don’t show the same topic ten times)
- don’t personalise sensitive areas unless the user opts in
If your team uses AI to generate copy for prompts or emails, Outreach’s notes on best practices for AI personalisation are a good reminder: state the tone, set boundaries, and don’t let the system invent details.
Fairness and quality checks: keep recommendations useful, balanced, and accurate
Recommendation systems can drift towards whatever gets quick clicks. That can mean fear, outrage, or shallow takes. It’s not because the model is evil. It’s because it’s obedient.
Guardrails that help:
- diversity targets (mix topics and formats)
- fresh-content boosts (don’t bury new reporting)
- source variety (avoid one-source monocultures)
- editorial “do-not-suppress” topics (public safety, major elections, urgent updates)
- testing for harm, like over-recommending misinformation-adjacent content
For news, “quality” is not just engagement. It’s also whether the system helps readers understand, not just react.
Measuring success: the numbers and tests that tell you it’s working
Personalisation can feel subjective. Measurement makes it concrete.
The best approach is small experiments, not a full rebuild. Test one module, one signal, one page. Keep the rest steady.
In 2026, more teams also use simple predictive signals to spot when readers are about to bounce. It’s not fortune-telling. It’s pattern spotting, like noticing that people who don’t find a useful second click in 20 seconds often leave.
Metrics that matter for content sites: reads, depth, returns, and saves
Track outcomes that reflect real value, not just curiosity clicks.
Here’s a clean set that works well for a news platform:
| Metric | What it tells you | Why it matters |
|---|---|---|
| Recommendation click-through rate | Are the suggestions tempting? | Measures relevance at a glance |
| Time to first useful click | Do readers find value quickly? | Reduces frustration and bounces |
| Scroll depth / engaged time | Are they actually reading? | Better than raw pageviews |
| Saves and follows | Did it feel worth keeping? | Strong satisfaction signal |
| Newsletter sign-ups | Are they ready to commit? | Builds return habits |
| Repeat visits (7-day, 30-day) | Do they come back? | The real test of personalisation |
| Churn / inactivity | Who stopped returning? | Points to weak experiences |
A warning on vanity metrics: raw clicks can rise while satisfaction falls. If recommendations become clickbait-y, you’ll see higher CTR but fewer saves, shorter sessions, and more fatigue.
WebFX has a straightforward look at AI content personalisation for marketing that’s helpful for thinking about measurement discipline, even if your goals are editorial.
Simple testing plan: A/B tests, holdouts, and what to change first
A/B testing just means two groups see two versions, and you compare results. Keep the wording and design as similar as possible, so you’re testing the recommendation logic, not a new colour.
A smart setup also uses a holdout group (a slice of users who keep the old experience). That proves the lift is real, not seasonal noise.
A safe first test for a news site:
- Version A: a generic “Popular” module
- Version B: a personalised “Recommended for you” module using saves and follows
Once that’s stable, test:
- CTA wording (“Follow topic” vs “Get updates”)
- topic mix rules (how much personal vs must-know)
- diversity limits (avoid repeating the same theme)
Quick checklist for clean tests:
- change one thing at a time
- run long enough to cover weekday and weekend behaviour
- watch for big news spikes that skew results
Conclusion
Personalising website content with AI recommendations works best when it feels like good service: quick, relevant, and never pushy. Place recommendations where they help most, use respectful first-party signals, and keep measuring what readers actually value, like saves and return visits.
Pick one page to start, choose one signal (saves or follows), and track one metric for two weeks. If readers find the next right story faster, you’ll see it in the numbers and you’ll feel it in the way they come back. That’s personalisation at its best.
