At MORAI, we understand that one size does not fit all. Our approach to building Recommendation Models is highly customizable to your business needs:
Data Collection: We gather and process user data to understand their preferences and behaviors.
Algorithm Selection: We carefully choose the most suitable recommendation algorithms, including collaborative filtering, content-based filtering, and hybrid models.
Model Training: Our data scientists employ advanced machine learning techniques to train recommendation models on your data.
Evaluation: We rigorously evaluate the performance of recommendation models to ensure they provide accurate and relevant suggestions.
Benefits:
![Enhanced User Engagement img](https://morai.eu/wp-content/uploads/2023/11/relationship.png)
Enhanced User Engagement
Recommendation models keep users engaged by offering content, products, or services that match their interests.
![Increased Conversions img](https://morai.eu/wp-content/uploads/2023/11/good-conversion-rate.png)
Increased Conversions
Personalized recommendations boost conversion rates and drive revenue growth.
![Customer Loyalty img](https://morai.eu/wp-content/uploads/2023/11/customer-experience.png)
Customer Loyalty
Tailored experiences foster customer loyalty, reducing churn and increasing retention.
![Data-Driven Decisions img](https://morai.eu/wp-content/uploads/2023/11/data.png)
Data-Driven Decisions
Access valuable insights about user preferences and behaviors, guiding your strategic decisions.
MORAI’s Success Stories:
As part of the R&D project, we built an AI model that recommends books to readers based on their personal tastes.
The project is available as a webapp called Readow.ai
![readow img](https://morai.eu/wp-content/uploads/2023/10/Zrzut-ekranu-2023-10-31-o-14.52.00.png)
The model provides unencumbered recommendations based on data regarding millions of readers and the books they read. The Self-Attentive Sequential Recommendation (SASRec) deep learning algorithm was used in this project.