Abstract
In online advertising systems, predicting the click-through rate (CTR) is an important task. Many studies only consider targeted advertisements in isolation, but do not focus on its relationship with other ads that may affect the CTR. We look at a variety of additional elements that can help with CTR prediction for tailored advertisements. We consider supplementary ads from two different angles: 1) the spatial domain, where contextual adverts on the same page as the target advertisements are considered and 2) from the perspective of the temporal component, where we assume people have previously clicked unclicked advertisements. The intuition is that contextual ads shown with targeted ads may influence each other. Also, advertisements that are connected reflect user preferences. Ads that are not connected may indicate to some extent what the user is not interested in. We propose a deep spatio-temporal neural network (DSTAN) for CTR prediction to use these auxiliary data effectively. Our model can decrease the noise in new data, learn the interaction between varied extra data and targeted advertisements, and fuse heterogeneous data into a coherent framework, highlighting important hidden information. Offline experiments on two public datasets show that DSTAN outperforms several of the most common methods in CTR prediction.
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References
Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th International Conference on World Wide Web (2007)
Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining. IEEE (2010)
Guo, H., et al.: DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017)
Lian, J., et al.: xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2018)
Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10, September 2016
Zhang, W., Du, T., Wang, J.: Deep learning over multi-field categorical data. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 45–57. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30671-1_4
Wang, R., et al.: Deep & cross network for ad click predictions. In: Proceedings of the ADKDD 2017, pp. 1–7 (2017)
Qu, Y., et al.: Product-based neural networks for user response prediction. In: 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE (2016)
Qu, Y., et al.: Product-based neural networks for user response prediction over multi-field categorical data, 37(1), 1–35 (2018)
Ouyang, W., et al.: Deep spatio-temporal neural networks for click-through rate prediction. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)
Zhou, G., et al.: Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019)
Xiao, Z., et al.: Deep multi-interest network for click-through rate prediction. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management (2020)
Covington, P., Adams, J., Sargin, E.: Deep neural networks for Youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems (2016)
Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2018)
Xu, W., et al.: Deep interest with hierarchical attention network for click-through rate prediction. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)
Feng, Y., et al.: Deep session interest network for click-through rate prediction. arXiv preprint arXiv:1905.06482 (2019)
Huang, J., et al.: Deep position-wise interaction network for CTR prediction. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1885–1889, July 2021
Mikolov, T., et al.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems (2013)
Guo, H., et al:. An embedding learning framework for numerical features in CTR prediction. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2021)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Xiao, J., et al.: Attentional factorization machines: learning the weight of feature inter actions via attention networks. arXiv preprint arXiv:1708.04617 (2017)
Ma, D., et al.: Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893 (2017)
Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfit ting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Duchi, J.C., Hazan, E., Singer, Y.J.J.o.M.L.R.: Adaptive subgradient methods adaptive subgradient methods for online learning and stochastic optimization, 12, 2121–2159 (2011)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008)
Xiong, C., et al.: Relational click prediction for sponsored search. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (2012)
Chung, J., et al.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Ouyang, W., et al.: Representation learning-assisted click-through rate prediction. arXiv preprintarXiv:1906.04365 (2019)
Lyu, Z., et al.: Deep match to rank model for personalized click-through rate prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)
Hidasi, B., et al.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
Huang, Z., Tao, M., Zhang, B.: Deep user match network for click-through rate prediction. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021)
Acknowledgment
This work was supported by the grant of the University Natural Science Research Project of Anhui KJ2019A0835, in part by the grant of Hefei College Postgraduate Innovation and Entrepreneurship Project, No. 21YCXL20.
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Li, XL., Gao, P., Lei, YY., Zhang, LX., Fang, LK. (2022). Deep Spatio-Temporal Attention Network for Click-Through Rate Prediction. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_51
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