Name
Abstract | ||
|---|---|---|
We investigate a class of emerging online marketing challenges in social networks; and formally, we define macro behavioral targeting (MBT) to be the marketing efforts that appeal to a massive targeted population with non-personalized broadcasting. Upon the problem formulation, we describe a probabilistic graphical model for MBT. In our model, we derive the prior distributions from scratch because existing applications of graphical model / Bayesian network cannot fully capture the unique characteristics of MBT. In the derivation, we propose an approximation method to circumvent an intractable situation where order statistics need be calculated from exponentially increasing computations. In the experiments, we present case studies on real Facebook data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2390131.2390135 | DUBMMSM |
Keywords | Field | DocType |
non-personalized broadcasting,bayesian network,order statistic,massive targeted population,intractable situation,probabilistic graphical model,approximation method,marketing effort,online marketing challenge,graphical model,behavioral targeting | Population,Data mining,Broadcasting,Behavioral targeting,Computer science,Online advertising,Bayesian network,Artificial intelligence,Graphical model,Probabilistic logic,Macro,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yusheng Xie | 1 | 95 | 14.63 |
| Yu Cheng | 2 | 615 | 55.76 |
| Daniel Honbo | 3 | 73 | 6.38 |
| Kunpeng Zhang | 4 | 61 | 12.54 |
| Ankit Agrawal | 5 | 607 | 59.22 |
| Alok N. Choudhary | 6 | 242 | 22.44 |
| Yi Gao | 7 | 1 | 0.69 |
| Jiangtao Gou | 8 | 1 | 0.35 |