An improved coverage pattern based ad-slot allocation framework for display advertising

Abstract

Advertising is a part of marketing activity and brings awareness to potential viewers about the prod- ucts/services. It helps businesses to increase sales and create a brand image of the products. Online advertising is a form of advertising which uses the Internet to promote products/services to viewers. With the proliferation of eCommerce, online advertising has become a popular mode of advertising. It enables a higher reach of users at reduced costs. Search engine advertising, display advertising, social media advertising, native advertising, video advertising, and email advertising are some of the modes of online advertising. In this thesis, we have made an effort to improve display advertising. Web banners or display advertising are important among the types of online advertising. Display advertising mainly consists of visitors, advertisers, and the publisher. The publisher owns the website (a collection of web pages). A web page contains ad slots. Advertisers are interested in placing the banners in the ad slots to expose banners to potential users. Normally, given a website, several advertisers bid for slots. Given the website, click stream data of user visits, and the demands of multiple advertisers for user visits, the issue is to develop an approach to allocating ad slots so that the maximum number of advertisers’ demands can be satisfied and the publisher’s revenue can be improved. The naive approach suffers from the issues of ad repeatability and underutilization of slots. Existing works addressed this problem by modeling it as an optimization problem by employing reinforcement learning and data mining techniques. Also, in the literature, there is an effort to solve the problem by exploiting the knowledge of coverage patterns and assuming a single slot per web page. However, there are multiple slots per page in practice, and the existing approach can not be extended to a practical scenario. In this thesis, we have tried to improve the existing coverage pattern-based approach, which has been proposed by assuming a single slot per page by considering a practical scenario, i.e., by considering multiple ad slots for each web page. As part of this effort, it was noted that the existing coverage pattern algorithms are not scalable to very large-size click-stream transactions. So, in this thesis, to improve the scalability of the existing approach, we have developed a distributed approach to mine coverage patterns; next, we have proposed an ad slot allocation approach by considering multiple ad slots per page. In the proposed distributed coverage pattern mining (DCPM) approach, we employ a notion of the summarized form of Inverse Transactional Database (ITD) and replicate it at every node. We also em- ploy an efficient clustering-based method to distribute the computational load among the Worker nodes. We have performed extensive experiments using two real-world datasets and one synthetic dataset. The results show that the proposed approach significantly improves the performance over the state-of-the-art approaches in terms of execution time. We have also proposed a framework for display advertising by considering multiple slots per page. In this framework, we employ the DCPM approach and compute the ad-slot patterns ASPs. We then calculate the impressions of each ASP and propose an efficient allocation approach to meet the advertiser demand in the form of impressions. Our extensive performance evaluation using two real-time click- stream datasets demonstrates the efficiency of the proposed framework in terms of improved publisher revenue and reduced ad repeatability. Ad revenue is vital for several e-commerce companies and revenue from display advertising is one of the opportunities. We have proposed a scalable pattern mining approach to improve the publisher’s rev- enue through display advertising. We hope that the proposed framework will facilitate the improvement of the approaches that are being followed by display advertisement companies