Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Allocating online advertisement space with unreliable estimates
Proceedings of the 8th ACM conference on Electronic commerce
Adaptive bidding for display advertising
Proceedings of the 18th international conference on World wide web
Discovering coverage patterns for banner advertisement placement
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Towards efficient discovery of coverage patterns in transactional databases
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
International Journal of Mobile Communications
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In an online banner advertising scenario, an advertiser expects that the banner advertisement should be displayed to certain percentage of web site visitors. In this context, to generate more revenue for a given web site, the publisher has to meet the demands of several advertisers by providing appropriate sets of web pages. To help the publishers and advertisers, in this paper, we propose a model of coverage patterns and a methodology to extract potential coverage patterns by analyzing click stream data. Given web pages of a site, a coverage pattern is a set of web pages visited by a certain percentage of visitors. The proposed approach has the potential to enable the publisher in meeting the demands of several advertisers. The efficiency and advantages of the proposed approach is shown by conducting experiments on real world data sets.