Daily fantasy sports allow sports fans to participate in the matches they are watching. The daily fantasy sports landscape is competitive and evolves fast. To see off their competitors, some operators now personalize their offerings to improve the engagement of their users and increase their revenues. This paper presents the development and implementation of a scaled and real-time recommender system at FanDuel for its Daily Fantasy Sports (DFS) users. The system aims to recommend contests to users based on their past contest entries, leveraging historical data and machine learning techniques.In this paper, we propose a scalable contest recommender model that was built on real-time data pipelines and is re-trained every hour. We identify key factors that influence user preferences and contest selection. The recommender system analyzes users’ historical data, identifies similar users based on their participation behavior, and suggests contests that align with their interests. On average, we recommend contests to 180K users daily and have recommended over 300 million contests to 2 million users since the model’s inception. Notably, we saw over 1000% lift in user conversion rates and 4.43% lift in overall entry fees, peaking at 15.6% in April.

The increasing availability of GPS-embedded devices has introduced a new dimension in digital market especially location-based services. In practice, the location data is used to understand and predict consumer mobility behavior and trend for various purposes. In this paper, we propose two methodologies to first identify suspicious location from consumer location data and to infer location at both individual device and device to device level based on systematic solution. Using stay-point clustering and suspicious patterns we identified from extensive analysis, 20-30% of records with location were observed to be suspicious. After removing inaccurate location data, we have employed scalable heuristic approach to backfill records with location even for devices that originally had no available location. Our model showed the accuracy within 50 meters at 95thpercentile across different countries, including Japan, Indonesia, India, and the United States with 10-15% increase in the number of records with location and 5-10% increase in new number of devices with location.

Democratization of information access brought about by digital distribution has resulted in two contradictory phenomena: the ability to personalize consumer experience, and greater anonymity of users. These intensify when information is consumed on mobile devices, particularly because techniques to profile users on desktop web do not work on mobile smart-devices. Yet, the already large and still fast-growing field of mobile advertising require activation of audience segments against mobile advertising campaigns. Of particular importance are age and gender segments of mobile users, as these user characteristics are required for targeting a large number of ad campaigns. To date, there are no practical methodologies available in the literature that allow for accurate identification of age and gender of mobile users, at scale.In this paper, we propose a scalable machine learning approach to infer the age and gender of mobile users. We have successfully tested and implemented the gender prediction model for 8 countries, additionally the groundwork has been laid out for implementation of age inference. The output is integrated with our commercial products, furthermore, it is used as a part of custom client deliveries. We inferred gender for more than 500 million devices and there is a notable increase in number of devices with gender label (post prediction) within our current dataset. We have also inferred age for 17 million devices in Australia.