Abstract
In this paper, we study how the application of a dynamic user engagement profiling can influence the efficiency of systems aimed at detecting the user’s propensity to buy a subscription. Specifically, we address a task of identifying the digital media readers who are involved enough in the publisher’s offer to pay for access to the content of a given webpage. We present the user engagement profile updating framework responsible for enriching raw events with time-agnostic temporal features. In particular, we experimentally evaluate the performance of machine learning algorithms for the task of predicting the user propensity to subscribe using the synthetic dataset based on publicly available data streams on users of KKBox’s music service. Additionally, we provide the results of online tests in which the propensity-to-subscribe prediction model is used to control the paywall displays on a digital media website with live traffic. The results of experiments have proven that enrichment of data with engagement profiles leads to higher performance of prediction models than relying just on raw features and tuning the model’s hyperparameters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
References
Andrew Ng: MLOps: From Model-centric to Data-centric AI - DeepLearning.AI. https://www.deeplearning.ai/wp-content/uploads/2021/06/MLOps-From-Model-centric-to-Data-centric-AI.pdf. Accessed Oct 2021
Apache Spark: PySpark ML Library. https://spark.apache.org/mllib/ (2021). Accessed Oct 2021
Aral, S., Dhillon, P.S.: Digital paywall design: implications for content demand and subscriptions. Manag. Sci. 67(4), 2381–2402 (2020). https://doi.org/10.1287/mnsc.2020.3650
Carlton, J., Brown, A., Jay, C., Keane, J.: Using interaction data to predict engagement with interactive media. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 1258–1266. ACM, New York, NY, USA (2021). https://doi.org/10.1145/3474085.3475631
Davoudi, H., An, A., Zihayat, M., Edall, G.: Adaptive paywall mechanism for digital news media. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 205–214. ACM, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219892
Davoudi, H., Rashidi, Z., An, A., Zihayat, M., Edall, G.: Paywall policy learning in digital news media. IEEE Trans. Knowl. Data Eng. 33(10), 3394–3409 (2021). https://doi.org/10.1109/TKDE.2020.2969419
Davoudi, H., Zihayat, M., An, A.: Time-aware subscription prediction model for user acquisition in digital news media. In: Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 135–143. SIAM (2017). https://doi.org/10.1137/1.9781611974973.16
Fei, Y., Lv, C., Feng, Y., Zhao, D.: Real-time filtering on interest profiles in twitter stream. In: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, pp. 263–264. JCDL 2016, ACM, New York, NY, USA (2016). https://doi.org/10.1145/2910896.2925462
Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, Cambridge, New York, NY, USA (2012)
Gregory, B.: Predicting customer churn: Extreme gradient boosting with temporal data. ArXiv (2018). https://www.arxiv.org/pdf/1802.03396.pdf
Grinberg, N.: Identifying modes of user engagement with online news and their relationship to information gain in text. In: Proceedings of the 2018 World Wide Web Conference, pp. 1745–1754. WWW 2018 (2018). https://doi.org/10.1145/3178876.3186180
Gupta, R., Parimi, R., Weed, Z., Kundra, P., Koneru, P., Koritala, P.: Driving subscriptions through user behavior modeling and prediction at bloomberg media. In: Nicosia, G., et al. (eds.) Machine Learning, Optimization, and Data Science. pp. 472–476. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-64583-0_42
Kaggle: WSDM - KKBox Churn Prediction Challenge. https://www.kaggle.com/c/kkbox-churn-prediction-challenge/. Accessed Oct 2021
KKBox: WSDM Cup 2018 Workshop within The 11th ACM International Conference on Web Search and Data Mining (WSDM). https://wsdm-cup-2018.kkbox.events/, Last accessed Oct. 2021
Li, H., Vu, Q.H., Pham, T.L., Nguyen, T.T., Chen, S., Lee, S.: An ensemble approach to streaming service churn prediction. In: WSDM Cup 2018 Workshop within The 11th ACM International Conference on Web Search and Data Mining, pp. 1–8. Los Angeles, California, USA (2018). https://wsdm-cup-2018.kkbox.events/
Liang, S.: Collaborative, dynamic and diversified user profiling. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 4269–4276 (2019). https://doi.org/10.1609/aaai.v33i01.33014269
Mezghani, M., Zayani, C.A., Amous, I., Péninou, A., Sèdes, F.: Dynamic enrichment of social users’ interests. In: 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS), pp. 1–11 (2014). https://doi.org/10.1109/RCIS.2014.6861066
Tang, X., Xu, Y., Geva, S.: Integrating time forgetting mechanisms into topic-based user interest profiling. In: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 1–4 (2013). https://doi.org/10.1109/WI-IAT.2013.132
Acknowledgments
This work was supported by the Polish National Centre for Research and Development, grant POIR.01.01.01-00-1352/17-00, and by Poznan University of Technology, grant 0311/SBAD/0703.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Misiorek, P., Warmuz, J., Kaczmarek, D., Ciesielczyk, M. (2022). Modeling User Engagement Profiles for Detection of Digital Subscription Propensity. In: Themistocleous, M., Papadaki, M. (eds) Information Systems. EMCIS 2021. Lecture Notes in Business Information Processing, vol 437. Springer, Cham. https://doi.org/10.1007/978-3-030-95947-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-95947-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95946-3
Online ISBN: 978-3-030-95947-0
eBook Packages: Computer ScienceComputer Science (R0)