Most so-called “personalized” experiences rely on outdated or coarse-grained data, causing recommendations to feel generic or repetitive. Shoppers expect instant recognition and meaningful suggestions, not one-size-fits-all results.

Static recommendation engines fail to adapt as customers browse, click, or abandon items. As a result, relevance decays quickly, click-through rates and average order values decline, and loyalty weakens.

Learn how to enable real-time AI personalization that incorporates continuously updated behavioral signals (recent searches, session context, and product interactions) directly into ranking and recommendation models. By using vector embeddings and online machine-learning inference, your system can refresh recommendations on every interaction, ensuring each customer sees content that truly reflects their intent in that moment.

Summary: Traditional personalization systems often rely on stale batch updates and fragmented infrastructure. This webinar explains how real-time signals, tensor-based ranking, and Vespa’s integrated platform improve ecommerce personalization.

Webinar Transcript: Solving the Personalization Problem in E-Commerce

Introduction

In our series on e-commerce challenges in the AI space, today I’m joined by Piotr Kobziakowski, our Senior Principal Solutions Architect, and myself, Jurgen. I’m responsible for the EMEA, Asia Pacific, and Japan region.

Today, we’re going to talk about the personalization problem and how to build a solution that meets today’s requirements.

A quick housekeeping note: if you have any questions, please feel free to send them to our email address, which you can find in the webinar resources. We’ll respond directly by email as soon as we can.

Today’s session will cover:

• A quick introduction and the core problem
• How to effectively build a personalization engine
• The models involved
• How to use e-commerce data more effectively
• How the ranking engine works in real time
• A live demo
• Why Vespa is a strong fit for this use case
• Key takeaways

Why Does Personalization Fail Shoppers Today?

When you think about the personalization problem today, one of the major reasons shoppers are underserved is that many implementations still rely on 24-hour batch cycles.

That means the data is often stale — not accurate, not current, and not reflective of what the shopper is doing right now.

Another issue is that personalization is often one-size-fits-all. Instead of responding to an individual shopper’s behavior, many systems personalize based on broad user groups and aggregate patterns.

This is often the result of fragmented systems where:

• search
• ranking
• personalization

all live separately.

Because those systems aren’t integrated, important signals can’t easily move from one part of the stack to another.

The result is a poor shopper experience.

For example, if someone searches for trousers, they may receive hundreds or thousands of results:

• the wrong size
• the wrong color
• sometimes even irrelevant products

This is where many of today’s systems fall short.

A Three-Step Approach to Fix It

If this sounds like your challenge, there’s a clear three-step approach you can take.

Step 1: Model All Your Data Together

The best practice we recommend is to model all relevant data into a single representation.

This includes:

• catalog data
• shopper behavior
• content
• context
• metadata tensors
• embeddings
• business rules
• inventory

Bring product attributes, shopper profiles, shopper history, and inventory into the same environment and use that as the foundation for your models.

Step 2: Capture More Signals in Real Time

Products and shopper behavior change every second.

Every click, dwell event, return, and stock update changes the relevance landscape.

If you can update your model in milliseconds, you significantly improve the shopper experience.

This includes signals like:

• session intent as it happens
• long-term preferences and affinities
• query context
• device
• location

Step 3: Surface Only What Matters

At query time, use those signals to surface only the data that is:

• relevant
• fresh
• personalized

This means showing results based on the individual shopper, not a shopper segment.

Recommendations should fit the moment the shopper is in right now, while still supporting your business goals.

Demo

With that foundation in place, Piotr is going to walk through exactly how to build a simple but highly effective real-time personalization engine.

He’ll take you through the architecture and then show a live demo of how it works in practice.

Building the Personalization Pipeline

Thank you, Jurgen.

Let’s break this down into a structured set of steps.

First, we need to extract enough information from the product itself.

We do this using AI vision models.

In the demo, you’ll see two different models:

• a VLLM model for product descriptions and feature extraction
• additional models for organizing metadata

From there, product attributes are represented in two ways:

Sparse Representation

This includes structured features extracted into sparse vectors.

Dense Representation

This captures brands, shapes, colors, and other product attributes in a dense vector space.

Once items are represented this way, every user interaction — every click, add-to-cart, or purchase — updates the user’s personalized representation.

Each interaction transfers some portion of product information into the user profile.

The more interactions there are, the richer the user preference model becomes.

That allows us to better match shoppers with the products they’re most likely to want.

Why Vespa

A major differentiator here is that Vespa is not just a vector database — it’s a tensor platform.

That means it can represent:

• scalars
• vectors
• matrices
• maps of vectors

all within a unified tensor framework.

This allows you to preserve complex data structures while still performing efficient mathematical operations.

You can integrate any model that can be exported to ONNX, including models from Hugging Face.

Because inference runs directly inside Vespa, you eliminate round-trip API latency between separate systems.

That significantly improves end-to-end performance.

Real-Time Ranking

Ranking is a core strength of the Vespa ecosystem.

The ranking layer allows you to combine:

• tensors
• inference outputs
• personalization signals
• business logic

into a single ranking profile.

That means you can test new ranking logic in parallel with production traffic, evaluate performance, and then hot-swap the profile in milliseconds.

This makes experimentation and A/B testing extremely fast.

Key Takeaways

Let’s summarize the main points.

Generic personalization costs you revenue.

Stale batch signals and one-size-fits-all recommendations lead to:

• lower relevance
• reduced click-through rates
• weaker loyalty
• lower revenue

Sparse tensors make shopper intent highly readable and easy to debug.

Unlike dense vectors alone, sparse representations allow direct updates without retraining models.

Most importantly, Vespa enables a real-time architecture.

With support for high write throughput and millisecond ranking decisions, you can incorporate:

• clicks
• favorites
• filters
• removals

in real time.

Finally, this all runs in one platform, not five separate services.

Retrieval, machine learning inference, personalization, merchandising controls, and stock logic all run inside the same system.

That means:

• no ETL bottlenecks
• no separate scoring services
• no lost signals
• lower latency

Closing

Thank you very much for joining us today.

If you have any questions, please send them to the webinar email address and we’ll get back to you as soon as possible.

Our next webinar in this series will be on May 20, where we’ll talk more about the signal integration problem.

Thank you again for listening, and we look forward to seeing you next time.