Development of a customized recommendation system for personalized media content

Challenge  

Our client, a leading media company, faced the challenge of personalizing the user experience of a newly created digital platform. The main issue was suggesting content to users that matched their individual preferences, was current, and was tailored to their geographical location. Without an effective recommendation system, there was a risk that the user experience would remain impersonal and the platform would lose attractiveness.

Approach  

To address these challenges, we developed a scalable cloud solution using modern machine learning methods and NLP techniques. Our approach included creating a recommendation engine that embeds user and article profiles. We implemented a fast and scalable vector search using approximate nearest neighbors and integrated named entity recognition and other NLP services. The entire solution was provided end-to-end and integrated into the app via the Google Cloud Platform.

Result  

By implementing this solution, the media company was able to provide its users with personalized recommendations, which directly resulted in improved user engagement and satisfaction. The platform was able to efficiently cater to individual user preferences, offering a dynamic and relevant content experience, leading to higher user activity and retention.