A practical guide to session based recommendations in 2025

Let's be honest: most of the "personalization" we see online is a little off. You buy a pair of socks one time, and suddenly your entire online experience for the next six months is a non-stop sock-a-thon. The issue is that old-school personalization is obsessed with your past, completely ignoring what you actually need right now.

But there's a much smarter way to figure out what someone wants in the moment: session based recommendations. This whole approach is about focusing on the present, making an educated guess about what a user will do next based on what they're doing in their current visit.

This guide will walk you through what session based recommendations are, why they matter for anyone in e-commerce or customer support, and how you can get similar, powerful results without needing a massive engineering team.

What are session based recommendations?

Session based recommendations are all about predicting a user's next move based on their clicks and actions within a single, continuous visit. Just think of a "session" as one trip to your website, including every product view, search, and item tossed into the cart during that time.

This is a complete flip from traditional methods like collaborative filtering, which rely on long-term user profiles and heaps of historical data. You’ve seen this a million times, it's the classic "people who bought this also bought that" logic. And while it has its place, it’s useless for new or anonymous visitors. If you don't know who someone is, you can't recommend anything. This is what folks in the industry call the "cold-start" problem.

Session-based models neatly solve this by zeroing in on the current context. It’s like a really good store clerk. They don't need your entire purchase history to be helpful. They just listen to what you're asking for and see what you're looking at now to make a smart suggestion. By analyzing what a user is doing in real-time, these systems can offer up relevant predictions from the very first click, whether they're dealing with a loyal customer or a first-time browser.

How do session based recommendations actually work?

The real cleverness here is in treating a user's journey as a sequence of events. The order of clicks, say, on a blue shirt, then a pair of jeans, then brown boots, tells a story about what that person is trying to do.

The main goal is something called "next event prediction." If a user's actions are "viewed item A, then item B," the system's job is to figure out the most likely next step. Will they look at item C, or will they add something to their cart?

In a lot of ways, data scientists treat this like a language problem. They frame a user's "session" as a "sentence" and the "items" they interact with as "words." This lets them use powerful AI models, many originally built for understanding human language, to learn the relationships between items. For instance, a model quickly picks up that "running shoes" and "athletic socks" tend to appear in the same "sentence," making them a logical pair to recommend together.

The data it needs is simple and immediate:

• User interactions: Clicks, views, add-to-carts, purchases.

• Timing: What someone looked at 30 seconds ago is way more relevant than what they saw 10 minutes ago.

• No history needed: It works without user profiles, login data, or those creepy tracking cookies.

This is exactly how a modern AI support agent thinks. It looks at the "session", a customer's question and the context around it, to predict the best "next event." That could be giving the right answer, flagging the ticket for a human, or looking up order info. With a tool like eesel AI, you can pull knowledge from your helpdesk, internal docs, and past tickets, which helps these predictions become incredibly accurate and genuinely useful.

eesel AI connects to all your company's knowledge sources to provide accurate, context-aware support, which is a practical application of session based recommendations.

Why session based recommendations are so important for modern customer experience

Thinking in terms of sessions isn't just about showing people more relevant products. It’s about making the entire customer experience better in ways that actually help your business.

Here’s why it’s such a big deal:

How session based recommendations solve the "cold-start" problem

This is the most immediate win. You can give new visitors helpful suggestions from their very first click. For an e-commerce store, this means someone new is more likely to find what they're looking for and make a purchase. For a support portal, it means an anonymous user gets a direct answer from a chatbot instead of getting lost in a maze of FAQs. It's a simple way to lower bounce rates and make a great first impression.

Adapt to what customers want now

A customer's needs are always changing. The person shopping for a kid's birthday gift today might be looking for a new work laptop tomorrow. Old, profile-based systems get stuck in the past, still pushing toy recommendations long after the birthday is over. Session-based models adapt on the fly because they only care about what's happening right now, making sure the experience always feels fresh and relevant.

Move the needle on business goals

When you show people what they want, when they want it, good things tend to happen.

• Higher conversion rates: Guiding a user to the right product or answer at just the right moment leads directly to more sales and happier customers.

• Better engagement: A smooth, personalized experience keeps people on your site longer and encourages them to explore.

• Stronger customer loyalty: People notice and appreciate it when a brand gets their immediate needs right without being intrusive. It builds trust and makes them want to come back.

A more privacy-friendly way to personalize

In an age of GDPR and general creepiness around data tracking, this is a huge plus. Since these methods don't need to track users over the long term or store tons of personal data, they offer a way to personalize the experience that respects user privacy.

The headaches of implementing session based recommendations (and a smarter way forward)

While the idea is great, the reality of building or buying a traditional recommendation engine is full of problems that most teams aren't ready for. It's one thing to read about it, but it's another thing entirely to get it working.

The technical complexity and cost barrier

First off, building these systems is not a weekend project. It requires a specialized team of data scientists and machine learning engineers, plus a serious investment in computing power. The models you read about in academic papers, things with names like Recurrent Neural Networks (RNNs) and Transformers, are incredibly complex and out of reach for most companies.

Even if you choose an off-the-shelf solution from a major cloud provider like Amazon Personalize or the now-retiring Azure AI Personalizer, you're not in the clear. These platforms often involve complicated setups, confusing pricing that can spiral out of control, and the risk of getting locked into one vendor's ecosystem. You end up handing over a key part of your customer experience to a black box you have very little control over.

The siloed knowledge problem

Here’s the biggest blind spot in the traditional approach: recommendation engines are great at suggesting products, but they're lost when a user's real intention is about something else entirely. What if they have a support question, a billing issue, or a feature idea?

Your company's real knowledge isn't just in a product database. It’s scattered everywhere: in your knowledge base, old support tickets, internal wikis on Confluence or Google Docs, and even in your team's Slack conversations. A standard recommendation engine can't touch any of that. This leads to a disconnected experience where your website can recommend the perfect product, but your chatbot can't answer a simple question about the return policy.

This infographic on knowledge integration shows how modern AI tools overcome the siloed knowledge problem mentioned in session based recommendations.

A modern alternative: Applying session intelligence to support

For most businesses, the end goal isn't just to recommend another product, it's to recommend the right solution. This is where modern AI support platforms completely change the conversation.

eesel AI gives you a practical way to use the core ideas of session-based intelligence without the soul-crushing engineering work. Instead of trying to guess the next product someone might buy, it focuses on what actually matters: figuring out the right answer or action to solve their problem on the spot.

It connects to all your knowledge sources in just a few clicks, which is a world away from rigid, siloed recommendation engines. It learns from your entire Zendesk or Freshdesk ticket history, your Confluence pages, and your Google Docs. It gets up to speed on your brand's voice and business context automatically, learning from every past "session" (or ticket) to give super-relevant answers.

And you can forget about long, painful implementation projects. The setup with eesel AI is completely self-serve. You can connect your helpdesk, point it to your docs, and have a working AI agent ready in minutes. No mandatory sales calls or waiting around for an engineering team.

One of the biggest fears with AI is launching something that gives terrible answers. eesel AI’s simulation mode gets rid of that risk. You can test your setup on thousands of your past tickets in a safe environment. You'll see exactly how it would have responded, giving you a clear forecast of its performance and your potential savings before it ever talks to a live customer. This lets you start small, maybe automating just one type of ticket, and then expand as you see the results.

The eesel AI simulation mode provides a risk-free way to test AI performance, a smarter approach than traditional session based recommendations implementation.

From recommending products to providing solutions

Understanding what a user needs in the moment is the key to a great customer experience. While traditional session based recommendations have mostly been about finding the next product to sell, the basic idea of real-time, contextual understanding is even more powerful when you apply it to customer support.

The future of customer interaction isn't just about showing people what to buy next; it's about giving them the answers they need, right when they need them. It's about solving problems instantly and turning frustrating moments into happy ones. If you’re ready to move beyond basic recommendations and start delivering intelligent, immediate solutions, it’s time to see what a dedicated AI support platform can do.

Try eesel AI for free and you can deploy an AI agent that learns from your existing knowledge in under five minutes.