Driving multifold improvement in recommendation efficiency
Our customer in Media Distribution Industry. Started a short form video app like TikTok. They used a commercial out-of-the-box recommendation system from a renowned multi-billion dollar firm. However, the recommendation efficiency of this system was dismal. With Normalized Discounted Cumulative Gain (NDCG) of 0.03, the recommendation engine efficiency was almost as good as random selection. Our customer approached us to tune their AI model and improve the recommendation efficiency. We improved the NDCG metric to 0.65. This was an important business problem to solve for our customer because better recommendations means, more time spent on the platform and improved ad revenues.

Download this case study to learn more about how our AI-model tuning improved the recommendation engine efficiency by a factor or 20x +