Recommendation Engine
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Recommendation Engine
AI-Powered
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 Overview
Recommendation Engine by WEBSENSA uses advanced AI filtering techniques to match users with content or products they are most likely to enjoy.
It analyses behavioural patterns, item similarities and user-item interactions to deliver tailored product suggestions across diverse digital platforms.
Designed for organisations aiming to maximise engagement and conversions, the engine enhances product discovery, content consumption and customer satisfaction.
Key Features
Key Components
01.
Content-Based Filtering
The engine analyses similarities between content or products. Using attributes such as title, description, author, category or other metadata, it recommends items similar to those a user has previously viewed, liked or purchased.
02.
Collaborative Filtering
By analysing user behaviour and patterns of engagement, the system identifies users with similar interests. It then recommends items that users with comparable characteristics have interacted with.
03.
Hybrid User-Item-Based Collaborative Filtering
The engine merges content-based and collaborative-filtering methods to deliver more accurate and personalised recommendations – leveraging both user preferences and item characteristics.
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Why Choose the Recommendation Engine?
Recommendation Engine increases user engagement and revenue by helping customers quickly discover relevant items and content.
It uses proven AI techniques to personalise experiences, improving satisfaction and encouraging users to return more often.
By analysing interactions, preferences and item attributes, it provides businesses with deeper insights into customer behaviour and supports smarter decision-making.
Industries Served
Research Teams We Serve
E-commerce and Retail
Suggest complementary products and trending items to support cross-selling and personalised shopping experiences.
Media and Entertainment
Recommend movies, playlists, series or articles based on user history and preferences to boost long-term engagement.
Travel and Hospitality
Offer travel recommendations tailored to user habits – such as favourite destinations, hotel types or past booking patterns.
Education and E-learning
Recommend new courses based on student interests and progress – supporting personalised learning experiences.
How to Get Started
Simply share your product catalogue, user interaction data and business goals. We analyse this and configure the most suitable recommendation models – content-based, collaborative, or hybrid. Integration is via API or SDK, and the system begins optimising results almost immediately.
Ready to increase engagement with personalised recommendations?
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Additional information
01.
PROJECT VALUE
4,633,997.51 PLN
02.
Contribution from European funds
3,485,668.54 PLN
03.
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".
