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STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
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Using data mining to profile TV viewers
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FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
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Behavioral targeting of content to users is a huge and lucrative business, valued as a $20 billion industry that is growing rapidly. So far dominant players in this field like Google and Yahoo examine the user requests coming to their servers and place appropriate ads based on the user's search keywords. Triple play service providers have access to all the traffic generated by the users and can generate more comprehensive profiles of users based on their TV, broadband and mobile usage. Using such multi-source profile information they can generate new revenue streams by smart targeting of ads to their users over multiple screens (computer, TV and mobile handset). This paper proposes methods to place targeted ads to a TV based on user's interests. It proposes an ad auction model that can leverage multi-source profile and can handle dynamic profile-based targeting like Google's AdWords vis-a-vis static demography-based targeting of legacy TV. We then propose a 0.502-competitive revenue maximizing scheduling algorithm that chooses a set of ads in each time slot and assigns users to one of these selected ads.