AI is only as smart as the data that feeds it.

At Datos, we capture real-world behavior across millions of users and billions of URLs – giving us a uniquely rich clickstream dataset that goes far beyond traditional, static sources.

Clickstream data is the digital footprint users leave behind as they interact with websites. Unlike isolated website logs and stats or scraped content, clickstream data reflects how users move across the web: what they search for, what links they follow, which domains they visit in sequence, and what actions they take along the way. 

What useful signals can be derived from clickstream?

Clickstream can be turned into a wide range of machine learning–friendly signals, which can be aggregated at different levels: by user, by item (e.g. URL or domain), or by query.

Some examples of useful signals include:

• Sequences of visited domains and pages
• Backlink or referral chains (how users arrived at a page)
• Common paths toward a goal (e.g., research → purchase)
• Pairs of queries and clicked results
• Conversion outcomes (purchase, signup, download)
• Search query reformulations or refinements
• Co-clicked or co-viewed items across users
• Bounce or return-to-search behavior

These signals form the foundation for enhancing recommender systems, semantic search engines, and LLM-based applications.

How Clickstream signals can be used for Recommender Systems

Recommender systems suggest products, content, or actions based on user behavior or preferences. They’re widely used in e-commerce, streaming, marketplaces, and social media. Recommender systems traditionally learn from your own on-site activity – clicks, cart adds, purchases – but this view is limited. External clickstream unlocks richer and more generalizable insight.

Clickstream helps recommender systems by enabling:

Cross-domain co-view/co-purchase patterns

Understand what users explore across different sites, not just your own. This builds richer item-to-item similarity maps that go beyond your catalog.

User journey modeling

By analyzing how typical users arrive at products – which domains are visited beforehand, what search terms are used, and what content is consumed along the way – you get insight into common paths toward conversion. This improves sequence-based models, enabling the system to recognize patterns that indicate high purchase intent.

Cold-start mitigation

For new users or items, external behavior helps build generalized profiles based on similar behavioral patterns across the web.

Session-based personalization

Clickstream sequences can be used to model short-term intent within a session. This enables real-time adaptation of recommendations based on current behavior, improving relevance without long-term history.

Clickstream enables recommender systems to move from isolated behavior to context-aware personalization, offering suggestions grounded in a broader view of user activity.

How Clickstream signals can be used for Semantic Search & Ranking

Semantic search is already a core feature of modern platforms, but clickstream data can take it further by anchoring search in real user behavior. It also delivers major improvements for keyword-based systems by helping bridge the gap between vague queries and useful results.

Improved query understanding

Clickstream reveals how users rephrase their searches – for example, going from “cheap flights” to “flights under £300”. These query reformulation patterns help train models to better interpret vague, ambiguous, or exploratory queries. This reduces failure rates and improves the chances of returning relevant results on the first try.

Training semantic embeddings

‘Query–click’ pairs help train embedding models that map queries and content into shared vector spaces, enabling relevance even when there’s no exact keyword overlap between the query and the document.

Providing better reranking features

Clickstream features like click-through rates for specific query-result pairs can become high-signal features in ranking models. They help reorder results not just by lexical match, but by actual usefulness.

Correcting bias in training data

Click behavior is often influenced by ranking position (position bias), not just relevance. Clickstream collected across multiple engines and SERPs helps disentangle true relevance from position effects. This allows for de-biased label generation when training learning-to-rank (LTR) models, leading to fairer and more accurate ranking algorithms.

Clickstream shifts ranking systems from matching words to understanding what people actually engage with, making results more relevant, personalized, and satisfying.

How Clickstream supports LLM-Based Systems

Large language models (LLMs) like GPT or BERT are trained on massive text corpora to learn general-purpose language understanding. Clickstream insights can improve LLM-based systems in meaningful ways with:

RAG (Retrieval-Augmented Generation)

Clickstream-derived signals (e.g., query – click frequency) are used to re-rank or weight documents during retrieval, before the LLM generates a response. This helps surface more relevant, user-validated content, improving grounding and reducing hallucinations without retraining the model.

Fine-tuning ranking models

Transformer-based models are fine-tuned using large-scale query-click datasets as weak supervision. This teaches models to rank content based on real user preferences.

Clickstream is especially powerful in hybrid systems. For example, an AI assistant might use an LLM to interpret a query while clickstream-trained models determine which products or answers to prioritize based on observed user behavior.

Clickstream-powered AI is the future

Clickstream data offers a powerful layer of behavioral insight that complements traditional training data. 

Whether you’re building recommendation engines, semantic search, or LLM-based systems, integrating real-world user behavior helps models align more closely with how people actually search, explore, and decide.

At Datos, we provide one of the largest and most diverse clickstream datasets available, spanning tens of millions of users and over 20 billion URLs from 185 countries. Our data helps teams move beyond static content and towards truly context-aware, behavior-driven AI.

Get in touch to find out more.