Intelligent Web Advertisement Based on Eye-Tracking and Machine Learning

Building and maintaining brand loyalty is a vital issue for market research departments. ‎Various means, including online advertising, helps with promoting loyalty to the brand amongst users. The present paper studies intelligent web advertisements with an ‎eye-tracking technique that calculates users’ eye movements, ‎gaze ‎points, and heat maps. This ‎paper ‎examines different ‎features of an online ad and their combinations, such as underlining words and personalization by eye-tracking. These characteristics include underlining, changing color, number of words, personalizing, inserting a related photograph, and changing the size and location of the advertisement on a website. They help advertisers to improve their ability to manage the ads by increasing users' attention. Moreover, the current research argues the impact of gender on users' visual behavior for advertising features in different Cognitive Demand (CD) levels of tasks while avoiding interruption of users’ cognitive processes with eye-tracking techniques. Also, it provides users the most relevant advertisement compatible with CD level of a task by Support Vector Machine (SVM) algorithm with high accuracy. This paper consists of two experiments that one of them has two phases. In the first and second experiments, a news website alongside an advertisement and an advertising website is shown to the users. The results of the first experiment revealed that personalizing and underlining the words of the ad grabs more attention from users in a low CD task. Furthermore, darkening the background promotes users' frequency of attention in a high CD task. By analyzing the impact of gender on users' visual behavior, males are attracted to the advertisement with red-colored words sooner than females during the high CD task. Females pay more prolonged and more frequent attention to the ads with red-colored words and larger sizes in the low CD task. The second experiment shows that the gazing start point of users with a right to left mother tongue language direction is mainly in the middle of the advertising website.