Data Modelling Method for Real-Time Advertising Service Based on Viewer Reaction and Intention in Online Broadcasting

Seongju Kang, Chaeeun Jeong, Kwangsue Chung

http://doi.org/10.5626/JOK.2020.47.11.1086

The interaction between the existing advertising service and the user is limited. To provide a personalized advertising service, advertisement systems should predict the user"s preference based on the user"s profile and the user-content relationship. Many recommendation schemes have been studied to predict the preferences of users. However, the existing recommendation system is difficult to guarantee real-time preference prediction as it performs a calculation of the matrix with high computational complexity. In this paper, we propose a data modeling method for real-time advertising services based on the reaction and intention of viewers. To predict the user"s preference in real-time, the user"s historical data is modeled in a tree structure. The tree structure allows us to retrieve and compare the data with logarithmic time complexity. To improve the accuracy of the recommendation, we have proposed a recommendation algorithm that considers both the user"s positive and negative evaluations. Finally, we have evaluated the performance of the proposed method through various methods.