TY - GEN
T1 - Selection and ordering of linear online video ads
AU - Kar, Wreetabrata
AU - Swaminathan, Viswanathan
AU - Albuquerque, Paulo
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/9/16
Y1 - 2015/9/16
N2 - This paper studies the selection and ordering of in-stream ads in videos shown in online content publishers. We propose an allocation algorithm that uses a collective measure of price and quality for each ad and factors in slot-specific continuation probabilities to maximize publisher revenue. The algorithm is based on cascade models and uses a dynamic programming method to assign linear (video) ads to slots in an online video. The approach accounts for the negative externality created by lower quality ads placed in a video, leading to viewer exit and thereby preventing the publisher from showing the subsequent ads scheduled in that session. Our algorithm is scalable and suited for real-time applications. A large log of viewer activity from a video ad platform is used to empirically test the algorithm. A series of simulations show that our algorithm, when compared to other algorithms currently practiced in industry, generates more revenue for the publisher and increases viewer retention.
AB - This paper studies the selection and ordering of in-stream ads in videos shown in online content publishers. We propose an allocation algorithm that uses a collective measure of price and quality for each ad and factors in slot-specific continuation probabilities to maximize publisher revenue. The algorithm is based on cascade models and uses a dynamic programming method to assign linear (video) ads to slots in an online video. The approach accounts for the negative externality created by lower quality ads placed in a video, leading to viewer exit and thereby preventing the publisher from showing the subsequent ads scheduled in that session. Our algorithm is scalable and suited for real-time applications. A large log of viewer activity from a video ad platform is used to empirically test the algorithm. A series of simulations show that our algorithm, when compared to other algorithms currently practiced in industry, generates more revenue for the publisher and increases viewer retention.
KW - Dynamic program
KW - Linear online video ads
KW - Revenue maximization
KW - Scalable real-time algorithm
KW - Viewer retention
UR - https://www.scopus.com/pages/publications/84962786116
U2 - 10.1145/2792838.2800194
DO - 10.1145/2792838.2800194
M3 - Conference contribution
AN - SCOPUS:84962786116
T3 - RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
SP - 203
EP - 210
BT - RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
PB - Association for Computing Machinery
T2 - 9th ACM Conference on Recommender Systems, RecSys 2015
Y2 - 16 September 2015 through 20 September 2015
ER -