A lifestyle-based approach for delivering personalized advertisements in digital interactive television

This paper presents a lifestyle-based approach for the delivery of personalized advertisements in digital interactive television. The theoretical basis of the approach is analyzed, and two variations are discussed. The first (segmentation variation) relies on interaction-based classification of users into lifestyle segments, while the second (similarities variation) is based on the identification of similarities among users based on demographic and TV program preferences data. In both variations, the user's interest is predicted by aggregating lifestyle neighbors' preferences. Results from an empirical validation, in the form of a laboratory experiment, are also presented in order to provide further evidence on the effectiveness and usefulness of the proposed approach when compared with machine learning algorithms, such as classification and nearest neighborhood. The superiority of the proposed approach is also demonstrated against user modeling evaluation methodologies, as well as against traditional marketing targeting practices.