Recommendation Engine

Recommendation Engine


Maximise Engagement with Presice Recommendations

Transform how your customers connect with your products and/or content. Our AI-driven Recommendation Engine leverages advanced filtering techniques, ensuring users discover items and media that resonate with their tastes.

Recommendation Engine

Recommendation Engine is an AI-powered solution that allows publishers, e-commerce shops and advertisers to create perfect-tailored recommendations of their products and content.

Recommendation Engine

Key features


Content-Based Filtering

Create recommmendations based on similarities between your content

The recommender analyses the similarity between your content or products. Based on set of information (in the case of an article: title, content, author, category, etc.), it recommends similar content to the one that the given user liked or read.


Collaborative Filtering

Recommend content based on similarities between your users

The recommender analyses both the similarity between users and their engagement towards the content or products. Based on that, it recommends the same or similar materials to people who show similar characteristics.


Hybrid User-Item-Based Collaborative Filtering

Combine content-based and collaborative-filtering

The newest tools aggregate these two approaches, so the results can be further improved. Recommendations can be based both on data about users preferences and interactions, and on the features of your products or services.

Recommendation Engine

Areas of application

E-commerce and Retail

Recommendation Engine boosts e-commerce by suggesting complementary products, like accessories pairing the chosen outfit, enhancing cross-selling opportunities. It personalises shopping by recommending trending items in user-favored categories based on past purchases.

Media and Entertainment

For media enthusiasts, the engine can recommend movies similar to their favourite genres or suggest playlists that include songs from beloved artists, enriching the experience. It might introduce users to new TV series based on their watch history, keeping the engagement high.

Travel and Hospitality

In travel, our system can suggest beachfront resorts to a user who frequently books coastal hotels or offer city break deals to those who enjoy urban exploration. It might also recommend culinary or cultural tours users have previously enjoyed, enhancing their travel experience.

Recommendation Engine

Game-Changing Benefits
for Your Company

Increased User Engagement

Boost user interactions by presenting relevant content

Revenue Growth

Increase sales thanks to matched product recommendations

Enhanced data analysis

Collect valuable data about customer preferences

Improved Customer Loyalty

Personalise content to keep customers coming back

Recommendation Engine

Product onboarding

What will we need to implement this solution?


Budget varies based on scale and customisation of the product.


Implementation depends on the project's complexity and client requirements.

Client Engagement

To make our system work properly for your business, we will ask for your user, product and service data.


The product ensures easy integration with various existing systems.

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Additional information



4,633,997.51 PLN


Contribution from European funds

3,485,668.54 PLN


Project implementation period

2020 – 2022

The development of the WARRP platform was co-financed by the European Union from the European Regional Development Fund within the Intelligent Development Program 2014 – 2020.

The project was realised as part of a competition by the National Centre for Research and Development: "Industrial research and development work carried out by enterprises," action: "Industrial research and development work conducted by enterprises."

logotypes of: WEBSENSA, NCBiR, European Funds, Rzeczpospolita Polska, Euopean Union