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.
Key Features
Key Components
01.
Content-Based Filtering
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.
02.
Collaborative Filtering
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.
03.
Hybrid User-Item-Based 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.
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Key features
01.
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.
02.
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.
03.
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.
04.
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
Industries Served
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?
How to Get Started
Let's talk about your project!
Contact us or use our interactive tool to estimate your project.
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," action: "Industrial research and development work conducted by enterprises."
