eSports Recommendation System
Boosting gaming and social experience in eSports platforms with constantly improving ML-backed recommendation systems.
Boosting gaming and social experience in eSports platforms with constantly improving ML-backed recommendation systems.
The eSports recommendation system is a part of a global platform for eSports competitions where all the athletes and other stakeholders can interact in a complete commercial ecosystem.
SOLVVE developed this system pursuing a customer's request to have an efficient tool for players that will not only engage them more with the eSports hub but also provide them with valuable data about their progress and opportunities to strategize better.
SOLVVE's machine learning department tackled the task of delivering personalized recommendations through three essential features.
First, the user scores component suggests potential matches by analyzing shared interests using mathematical algorithms, creating a prioritized list of recommendations.
Second, the weights recalculations feature adapts to the dynamic eSports platform by analyzing past events to update and improve the recommendation system with fresh data.
Lastly, a test model ensures recommendation system stability by comparing real and expected results using a test dataset.
PostgreSQL, Python, Flask, Docker