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Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Published: 19 October 2020 Publication History

Abstract

Prospective display advertising poses a particular challenge for large advertising platforms. The existing machine learning algorithms are easily biased towards the highly predictable retargeting events that are often non-eligible for the prospective campaigns, thus exhibiting a decline in advertising performance. To that end, efforts are made to design powerful models that can learn from signals of various strength and temporal impact collected about each user from different data sources and provide a good quality and early estimation of users' conversion rates. In this study, we propose a novel deep time-aware approach designed to model sequences of users' activities and capture implicit temporal signals of users' conversion intents. On several real-world datasets, we show that the proposed approach consistently outperforms other, previously proposed approaches by a significant margin while providing interpretability of signal impact to conversion probability.

Supplementary Material

MP4 File (3340531.3412739.mp4)
Prospective display advertising poses a particular challenge for large advertising platforms. We present a solution for addressing some of the biggest challenges in prospective advertising along with a novel deep time-aware approach designed to model sequences of users? activities and capture implicit temporal signals of users? conversion intents. On several real-world datasets, we show that the proposed approach consistently outperforms other competitive approaches by a significant margin.

References

[1]
S. K. Arava, C. Dong, Z. Yan, A. Pani, et al. Deep neural net with attention for multi-channel multi-touch attribution. arXiv preprint arXiv:1809.02230, 2018.
[2]
T. Bai, S. Zhang, B. L. Egleston, and S. Vucetic. Interpretable representation learning for healthcare via capturing disease progression through time. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 43--51. ACM, 2018.
[3]
A. Beutel, P. Covington, S. Jain, C. Xu, J. Li, V. Gatto, and E. H. Chi. Latent cross: Making use of context in recurrent recommender systems. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pages 46--54. ACM, 2018.
[4]
X. H. Cao, C. Han, and Z. Obradovic. Learning a dynamic-based representation for multivariate biomarker time series classifications. In 2018 IEEE International Conference on Healthcare Informatics (ICHI), pages 163--173. IEEE, 2018.
[5]
Y. Cui, R. Tobossi, and O. Vigouroux. Modelling customer online behaviours with neural networks: applications to conversion prediction and advertising retargeting. arXiv preprint arXiv:1804.07669, 2018.
[6]
D. Gligorijevic, J. Stojanovic, A. Raghuveer, M. Grbovic, and Z. Obradovic. Modeling mobile user actions for purchase recommendations using deep memory networks. In 41st Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval, 2018.
[7]
D. Gligorijevic, J. Stojanovic, W. Satz, I. Stojkovic, K. Schreyer, D. Del Portal, and Z. Obradovic. Deep attention model for triage of emergency department patients. In 2018 SIAM International Conference on Data Mining (SDM 2018), 2018.
[8]
J. Gligorijevic, D. Gligorijevic, I. Stojkovic, X. Bai, A. Goyal, and Z. Obradovic. Deeply supervised model for click-through rate prediction in sponsored search. Data Mining and Knowledge Discovery, Apr 2019.
[9]
N. Grislain, N. Perrin, and A. Thabault. Recurrent neural networks for stochastic control in real-time bidding. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19, pages 2801--2809, New York, NY, USA, 2019. ACM.
[10]
H. Jing and A. J. Smola. Neural survival recommender. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pages 515--524. ACM, 2017.
[11]
N. Karlsson. Control problems in online advertising and benefits of randomized bidding strategies. European Journal of Control, 30:31--49, 2016.
[12]
Y. Li, N. Du, and S. Bengio. Time-dependent representation for neural event sequence prediction. arXiv preprint arXiv:1708.00065, 2017.
[13]
H. B. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin, et al. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1222--1230. ACM, 2013.
[14]
B. N., K. R., and M. S. A large scale prediction engine for app install clicks and conversions. In International Conference on Information and Knowledge Management (CIKM), 2017.
[15]
J. Pan, Y. Mao, A. L. Ruiz, Y. Sun, and A. Flores. Predicting different types of conversions with multi-task learning in online advertising. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1834--1842. ACM, 2019.
[16]
W. Pei and D. M. Tax. Unsupervised learning of sequence representations by autoencoders. arXiv preprint arXiv:1804.00946, 2018.
[17]
A. Rajkomar, E. Oren, K. Chen, A. M. Dai, N. Hajaj, M. Hardt, P. J. Liu, X. Liu, J. Marcus, M. Sun, et al. Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1):18, 2018.
[18]
Y. Shan, T. R. Hoens, J. Jiao, H. Wang, D. Yu, and J. Mao. Deep crossing: Web-scale modeling without manually crafted combinatorial features. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 255--262. ACM, 2016.
[19]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998--6008, 2017.
[20]
S. Zhai, K.-h. Chang, R. Zhang, and Z. M. Zhang. Deepintent: Learning attentions for online advertising with recurrent neural networks. In 22nd ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pages 1295--1304. ACM, 2016.
[21]
Y. Zhang, H. Dai, C. Xu, J. Feng, T. Wang, J. Bian, B. Wang, and T.-Y. Liu. Sequential click prediction for sponsored search with recurrent neural networks. In Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.
[22]
Y. Zhou, S. Mishra, J. Gligorijevic, T. Bhatia, and N. Bhamidipati. Understanding consumer journey using attention based recurrent neural networks. KDD, 2019.
[23]
Y. Zhu, H. Li, Y. Liao, B. Wang, Z. Guan, H. Liu, and D. Cai. What to do next: Modeling user behaviors by time-lstm. In IJCAI, pages 3602--3608, 2017.

Cited By

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  • (2024)Deep Journey Hierarchical Attention Networks for Conversion Predictions in Digital MarketingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680066(4358-4365)Online publication date: 21-Oct-2024
  • (2022)Predicting Actions of Users Using Heterogeneous Online SignalsBig Data10.1089/big.2021.032010:4(298-312)Online publication date: 1-Aug-2022

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        cover image ACM Conferences
        CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
        October 2020
        3619 pages
        ISBN:9781450368599
        DOI:10.1145/3340531
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 19 October 2020

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        1. deep learning
        2. prospective advertising
        3. time-aware prediction

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        • (2024)Deep Journey Hierarchical Attention Networks for Conversion Predictions in Digital MarketingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680066(4358-4365)Online publication date: 21-Oct-2024
        • (2022)Predicting Actions of Users Using Heterogeneous Online SignalsBig Data10.1089/big.2021.032010:4(298-312)Online publication date: 1-Aug-2022

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