Driving Engagement in Daily Fantasy Sports with a Scalable and Urgency-Aware Ranking Engine

Researcher Unmesh Padalkar has published a paper detailing a novel AI recommendation engine specifically designed for the high-stakes, time-sensitive world of daily fantasy sports (DFS). The core challenge is that users must act within a narrow window before a real-world game begins, making traditional, static recommendation systems ineffective. To solve this, Padalkar adapted the Deep Interest Network (DIN) architecture by injecting temporality at two key levels: first, by adding real-time urgency features for each match (like 'time-to-round-lock'), and second, by using temporal positional encodings that let the model dynamically weigh the recency of a user's past interactions.

This urgency-aware model was trained using a listwise neuralNDCG loss function on a massive industrial-scale dataset containing over 650,000 users and more than 100 billion historical interactions. The training infrastructure was built for scale using a multi-node, multi-GPU setup on Ray and PyTorch. The results were significant, with the new model achieving a +9% improvement in the nDCG@1 ranking metric compared to a heavily optimized LightGBM baseline that used handcrafted features. This strong offline performance validates the model's design and establishes it as a viable core component for a planned on-device (edge) recommendation system, where it will undergo live A/B testing to drive user engagement and prevent revenue loss from missed opportunities.