An Inference Model for Online Media Users

Abstract

Watching videos online has become a popular activity for people around the world. To be able to manage revenue from online advertising an efficient Ad server that can match advertisement to targeted users is needed. In general the users’ demographics are provided to an Ad server by an inference engine which infers users’ demographics based on a profile reasoning technique. Rich media streaming through broadband networks has made significant impact on how online television users’ profiles reasoning can be implemented. Compared to traditional broadcasting services such as satellite and cable, broadcasting through broadband networks enables bidirectional communication between users and content providers. In this paper, a user profile reasoning technique based on a logistic regression model is introduced. The inference model takes into account genre preferences and viewing time from users in different age/gender groups. Historical viewing data were used to train and build the model. Different input data processing and model building strategies are discussed. Also, experimental results are provided to show how effective the proposed technique is.