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.
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 messages that they are most likely to be interested in, offers marketers the opportunity to increase the accuracy of their targeting, while at the same time providing viewers with messages that increase their satisfaction in terms of interest in the advertised product, thus increasing the message's communication effect.
The work reported so far in personalization over iTV platforms mainly concerns personalized recommendation of TV programs (e.g. Ardissino, Portis, Torasso, Bellifemine, Chiarotto, & Difino, 2001; Das & Horst, 1998; Gutta, Kuparati, Lee, Martino, Schaffer, & Zimmerman, 2000; Smyth & Cotter, 2000), personalized news (Maybury, 2001), personalized interactive documentaries (Nardon, Pianesi, & Zancanaro, 2002), and adaptive learning over Digital TV (Masthoff & Luckin, 2002). In this paper we built upon and extend the previous results of lifestyle based classification reported in Lekakos and Giaglis, 2002. More specifically, in the next section the context and background work of our research are presented, followed by an analytical presentation of our approach. Further user modeling issues, including the data acquisition mechanism, are then presented, followed by experimental results towards assessing the effectiveness of the proposed approach. The paper concludes with a discussion on achievements, limitations, and further research issues.
Context and Background Work
In order to design an effective personalization approach we consider targeting methods from marketing and advertising literature, and combine them with personalization methods from the literature of adaptive systems. As will be shown, methods and techniques from both domains can act in concert to overcome limitations of each method.
Personalization can be approached from many different angles depending on the unique characteristics and attributes of the application domain considered. For example, personalization of advertisements in TV environments can be...
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