An optimal algorithm for on-line bipartite matching
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
Proceedings of the 8th international conference on Intelligent user interfaces
Using data mining to profile TV viewers
Communications of the ACM - Mobile computing opportunities and challenges
Multi-unit auctions with budget-constrained bidders
Proceedings of the 6th ACM conference on Electronic commerce
AdWords and Generalized On-line Matching
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Improved Bounds for Online Routing and Packing Via a Primal-Dual Approach
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
IPTV Crash Course
Allocating online advertisement space with unreliable estimates
Proceedings of the 8th ACM conference on Electronic commerce
Online primal-dual algorithms for maximizing ad-auctions revenue
ESA'07 Proceedings of the 15th annual European conference on Algorithms
Online primal-dual algorithms for covering and packing problems
ESA'05 Proceedings of the 13th annual European conference on Algorithms
| Hi-index | 0.00 |
Behavioral targeting of content to users is a huge and lucrative business, valued as a $20 billion industry that is growing rapidly. So far, the 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 multisource 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 multisource profile and can handle dynamic profile-based targeting like Google's AdWords vis-à-vis static demography-based targeting of legacy TV. We then present 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.