{"type":"ScholarlyArticle","title":"An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration","authors":[{"name":"Yumin Su","slug":"yumin-su","given":"Yumin","family":"Su","affiliation":""},{"name":"Liang Zhang","slug":"liang-zhang","given":"Liang","family":"Zhang","affiliation":""},{"name":"Quanyu Dai","slug":"quanyu-dai","given":"Quanyu","family":"Dai","affiliation":"The Hong Kong Polytechnic University ,"},{"name":"Bo Zhang","slug":"bo-zhang","given":"Bo","family":"Zhang","affiliation":""},{"name":"Jinyao Yan","slug":"jinyao-yan","given":"Jinyao","family":"Yan","affiliation":"Communication University of China"},{"name":"Dan Wang","slug":"dan-wang","given":"Dan","family":"Wang","affiliation":"The Hong Kong Polytechnic University ,"},{"name":"Yongjun Bao","slug":"yongjun-bao","given":"Yongjun","family":"Bao","affiliation":""},{"name":"Sulong Xu","slug":"sulong-xu","given":"Sulong","family":"Xu","affiliation":""},{"name":"Yang He","slug":"yang-he","given":"Yang","family":"He","affiliation":""},{"name":"Weipeng Yan","slug":"weipeng-yan","given":"Weipeng","family":"Yan","affiliation":"Communication University of China"}],"abstract":"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 framework to tackle the two challenges. Specifically, we extract the pre-trained embedding from impressions/clicks to assist in conversion models and propose an inner/self-attention mechanism to capture the fine-grained personalized product purchase interests from the sequential click data. Besides, to overcome the time-delay issue, we calibrate the delay model by learning dynamic hazard function with the abundant post-click data more in line with the real distribution. Empirical experiments with real-world user behavior data prove the effectiveness of the proposed method.","doi":"10.24963/ijcai.2020/487","url":"https://ijcai.org/proceedings/2020/487","pdf":"https://ijcai.org/proceedings/2020/0487.pdf","markdown":"https://ijcai.org/proceedings/2020/487.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/487","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":487,"pages":{"first":3522,"last":3528},"keywords":["Humans and AI: Personalization and User Modeling","Multidisciplinary Topics and Applications: Information Retrieval"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25376/file_25376.m3u8","length_sec":289,"provider":"hls"}],"references":[{"position":1,"raw":"[Abadi et al., 2016] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. 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