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.
Enhanced User Engagement
Recommendation models keep users engaged by offering content, products, or services that match their interests.
Personalized recommendations boost conversion rates and drive revenue growth.
Tailored experiences foster customer loyalty, reducing churn and increasing retention.
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
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.