Name
Abstract | ||
|---|---|---|
Click-through rate (CTR) prediction is the core problem of building advertising systems. Most existing state-of-the-art approaches model CTR prediction as binary classification problems, where displayed events with and without click feedbacks are respectively considered as positive and negative instances for training and offline validation. However, due to the selection mechanism applied in most advertising systems, a selection bias exists between distributions of displayed and non-displayed events. Conventional CTR models ignoring the bias may have inaccurate predictions and cause a loss of the revenue. To alleviate the bias, we need to conduct counterfactual learning by considering not only displayed events but also non-displayed events. In this paper, through a review of existing approaches of counterfactual learning, we point out some difficulties for applying these approaches for CTR prediction in a real-world advertising system. To overcome these difficulties, we propose a novel framework for counterfactual CTR prediction. In experiments, we compare our proposed framework against state-of-the-art conventional CTR models and existing counterfactual learning approaches. Experimental results show significant improvements.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357384.3358058 | Proceedings of the 28th ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
counterfactual learning, ctr prediction, missing not at random, recommender system, selection bias | Recommender system,Data mining,Information retrieval,Computer science,Selection bias | Conference |
ISBN | Citations | PageRank |
978-1-4503-6976-3 | 8 | 0.53 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bowen Yuan | 1 | 8 | 1.54 |
| Jui-Yang Hsia | 2 | 9 | 0.88 |
| Meng-Yuan Yang | 3 | 8 | 0.53 |
| Hong Zhu | 4 | 9 | 2.23 |
| Chih-Yao Chang | 5 | 8 | 1.20 |
| Zhenhua Dong | 6 | 91 | 9.03 |
| Jen-Chih Lin | 7 | 24 | 8.22 |