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An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Conversion rate (CVR) prediction is becoming increasingly important in the multi-billion dollar online display advertising industry. It has two major challenges: firstly, the scarce user history data is very complicated and non-linear; secondly, the time delay between the clicks and the corresponding conversions can be very large, e.g., ranging from seconds to weeks. Existing models usually suffer from such scarce and delayed conversion behaviors. In this paper, we propose a novel deep learning
doi:10.24963/ijcai.2020/483
dblp:conf/ijcai/PiazzoniCSD20
fatcat:5fwdjw23qjeyfgcdeju6upd6km