Publicação
Deep learning frameworks for enhanced user engagement prediction
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | |
| dc.contributor.advisor | Shen, Yufei | |
| dc.contributor.author | Guo, Zhanshuo | |
| dc.date.accessioned | 2026-05-28T16:38:18Z | |
| dc.date.available | 2026-05-28T16:38:18Z | |
| dc.date.issued | 2026-01-14 | |
| dc.date.submitted | 2025-12-17 | |
| dc.description.abstract | This work demonstrates the individual contribution of Zhanshuo Guo in the field lab project “How Early Can We Predict Churn? Short-Window Engagement Forecasting in a Hybrid UGC Music Platform”. While chapters 1-3 summarize the collective effort of the group, chapter 4 dives into the deep learning approaches of the project. Using impression-level data from NetEase Cloud Music recently launched UGC module “Cloud Village”, this work aims at providing a scalable solution for predicting user engagement from short-term behaviors, which can help the management team to handle high user mobility issue and design win-back strategies. We employ both machine and deep learning models across two targets: binary churn and multiclass engagement. The cross design provides complementary perspectives on user behaviors, enabling performance indicators from one task to enrich the other. The result contributes to a more robust understanding of engagement prediction and operational values. | eng |
| dc.identifier.tid | 204242487 | |
| dc.identifier.uri | http://hdl.handle.net/10362/203562 | |
| dc.language.iso | eng | |
| dc.relation | UID/00124/2025 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Deep learning | |
| dc.subject | Churn prediction | |
| dc.subject | Long short-term emory | |
| dc.subject | Sequence processing | |
| dc.title | Deep learning frameworks for enhanced user engagement prediction | eng |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics |