Lifestyle
Introduction of Lifestyle advertising,
Literature review, Hypothesis, Research methodology.
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.
Introduction
The vast majority of existing research on personalization is concerned with computer-based systems of some kind or other. In this paper, we discuss the potential application of personalization principles in the context of 30-sec advertisements shown to viewers in a television environment. Personalization of advertisements in Interactive TV (iTV) refers to the delivery of advertisements tailored to the individual viewer's profile on the basis of user needs and interests. Several studies have revealed (Hawkins, Best, & Coney, 1998; iMedia, 2001) that less than 20% of the viewers are happy with the broadcasted advertisements. Indeed, the majority of viewers find them annoying and intrusive to their primary objective, which is to be entertained or informed through watching TV programs. Personalizing advertisements, i.e. providing viewers with
Literature review, Hypothesis, Research methodology.
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.
Introduction
The vast majority of existing research on personalization is concerned with computer-based systems of some kind or other. In this paper, we discuss the potential application of personalization principles in the context of 30-sec advertisements shown to viewers in a television environment. Personalization of advertisements in Interactive TV (iTV) refers to the delivery of advertisements tailored to the individual viewer's profile on the basis of user needs and interests. Several studies have revealed (Hawkins, Best, & Coney, 1998; iMedia, 2001) that less than 20% of the viewers are happy with the broadcasted advertisements. Indeed, the majority of viewers find them annoying and intrusive to their primary objective, which is to be entertained or informed through watching TV programs. Personalizing advertisements, i.e. providing viewers with