Unintrusive Customization Techniques for Web Advertising

Most online advertisement systems in place today use the concept of consumer targeting: Each user is identified and, according to his or her system setup, browsing habits and available off-line information, categorized in order to customize the advertisements for highest user responsiveness. This constant monitoring of a user’s online habits, together with the trend to centralize this data and link it with other databases, continuously nurtures fears about the growing lack of privacy in a networked society. In this paper, we propose a novel technique of adapting online advertisement to a user’s short term interests in a non-intrusive way. As a proof-of-concept we implemented a dynamic advertisement selection system able to deliver customized advertisements to users of an online search service or Web directory. No user-specific data elements are collected or stored at any time. Initial experiments indicate that the system is able to improve the average click-through rate substantially compared to random selection methods.