Name
Abstract | ||
|---|---|---|
ABSTRACT Click-through rate (CTR) prediction is a core problem of building advertising systems. In many real-world applications, because an ad placed in various positions has different click probabilities, the position information should be considered in both training and prediction. For such position-aware systems, existing approaches learn CTR models from clicks/not-clicks on historically displayed events by leveraging the position information in different ways. In this work, we explain that these approaches may give a heavily biased model. We first point out that in position-aware systems, two different types of selection biases coexist in displayed events. Secondly, we explain that some approaches attempting to eliminate the position effect from clicks/not-clicks may possess an additional bias. Finally, to obtain an unbiased CTR model for position-aware systems, we propose a novel counterfactual learning framework. Experiments confirm both our analysis on selection biases and the effectiveness of our proposed counterfactual learning framework. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3383313.3412241 | RECSYS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bowen Yuan | 1 | 8 | 1.54 |
| Yaxu Liu | 2 | 0 | 0.68 |
| Jui-Yang Hsia | 3 | 0 | 0.34 |
| Zhenhua Dong | 4 | 91 | 9.03 |
| Jen-Chih Lin | 5 | 24 | 8.22 |