<p>Display advertising is one of the predominant modes of online advertising. Given a set of available ad slots on the web pages, a publisher makes efforts to allocate them to advertisers, satisfying their demand and maximizing their revenue. Investigating efficient approaches for ad slot allocation to advertisers is a research issue. Given a set of ad slots from different pages, advertisers aim to deliver the ad to a specified number of targeted users. Exposing a user repeatedly to the same ad may result in a potential loss of revenue for the publisher and create boredom for the user. There is an opportunity to improve the publisher’s revenue by ensuring that each ad slot allocated to an advertiser reaches a distinct number of users. In the literature, efforts are being made to address this problem by modeling it as an optimization problem. These approaches suffer from the issues of complexity and practicality. We approach the problem with the pattern-mining framework by employing the notions of “coverage” and “overlap”. In the proposed approach, we exploit the knowledge of coverage patterns extracted from click stream transactions and propose a comprehensive, efficient, and practical ad slot allocation framework. The experimental results on two real-world click stream datasets show that the proposed approach could improve the revenue of the publisher by meeting the demands of the increased number of advertisers by reducing the repeated display of advertisements to users.</p>