Real-Time Recommendations

ERASMUS+ project designed to bridge academia and the startup ecosystem, developing a platform and educational programs to foster innovation and entrepreneurial skills among researchers and scientists.

Successfully developed and deployed multiple personalized content recommendation use cases using the ScaR recommender framework for the Virtual House of Digitalization, enhancing content discovery and user engagement.

Developed and deployed a personalized job matchmaking recommender system for Studo, later spun off into the standalone platform Talto, significantly enhancing student engagement and job application rates.

Developed a real-time data pipeline and integrated an outlier detection system to optimize garment placement in retail stores, improving sales performance. Built with Java, Spring Boot, RabbitMQ, Apache Solr, Python, and sklearn.

Core contributor to ScaR, an in-house scalable recommender framework following the microservices architecture, supporting real-time recommendations and streaming data processing using Java, Apache Solr, Spring Boot, Docker, and Jenkins.