Data collection by the people, for the people
Christine Robson, Sean Kandel, et al.
CHI 2011
We propose and evaluate a number of improvements to the linear programming formulation of web advertisement scheduling, which we have proposed elsewhere together with our colleagues [Langheinrich et al., 9]. In particular, we address a couple of important technical challenges having to do with the estimation of click-through rates and optimization of display probabilities (the exploration-exploitation trade-off and the issue of data sparseness and sealability). as well as practical aspects that are essential for successful deployment of this approach (the issues of multi-impressions and inventory management). We propose solutions to each of these issues, and assess their effectiveness by running large-scale simulation experiments. © 2005 Springer Science + Business Media. Inc.
Christine Robson, Sean Kandel, et al.
CHI 2011
Buse Korkmaz, Rahul Nair, et al.
AAAI 2025
Ge Gao, Xi Yang, et al.
AAAI 2024
Cameron S. Miner, Denise M. Chan, et al.
CHI EA 2001