The results of the study show that multi-objective personalized policies can significantly improve both ad and video consumption outcomes over single-objective policies. The algorithm was able to increase ad consumption by 61% at the expense of only a 4% decrease in video consumption. Similarly, compared to the single-objective policy optimized for ad consumption, there is a multi-objective policy that increases video consumption by 47% while decreasing ad consumption by just 13%.
“We are excited to have partnered with VDO.AI on this study and to have contributed to the development of multi-objective personalization algorithms that can significantly improve both ad and video consumption outcomes over single-objective policies,” said Omid Rafieian, Assistant Professor at Cornell University - Cornell Tech NYC.
Anuj Kapoor, Professor at the Indian Institute of Management Ahmedabad, added, “The study's findings have important practical implications for platform decision-making in real-time, and we are excited to have contributed to this important research.”
Founder & CEO at VDO.AI, Amitt Sharma said, “We are thrilled to have collaborated with Cornell University and the Indian Institute of Management Ahmedabad on this groundbreaking study. Our multi-objective personalization algorithms will revolutionize the way digital video advertising is approached, and we are excited to see how they will be implemented in real-time.”
The study was conducted using field experiments and utilized machine learning and causal inference techniques to analyze the data. The study's findings have significant practical implications for platform decision-making in real-time.
