Research on User Retention Management for Content-Based Media Platforms in the Algorithmic Recommendation Era

Content-based media platforms, such as video streaming services and news applications, are confronted with fierce competition in user retention within the context of algorithmic recommendation dominance. While recommendation systems effectively enhance short-term user engagement, they often fall short in addressing long-term retention due to an over-reliance on clickstream data, a limitation that leads to the neglect of user psychological needs, dynamic changes in content quality, and the impact of social interactions. This study proposes a multimodal neural network framework designed to integrate heterogeneous data, including user behavior sequences (such as clicks and watch time), content semantic features (including text attributes and audio-visual characteristics), and social network interactions, for the purpose of predicting user retention and formulating personalized retention strategies. The framework adopts modality-specific encoders, where Transformer is used for temporal behavior data, CNN for content features, and GNN for social graphs, combined with cross-modal attention mechanisms to capture synergistic relationships such as the resonance between content and user interests. A multi-task learning objective is employed to simultaneously predict retention status (whether users remain active within 30 days) and time-to-churn, thereby enhancing practical applicability. Experimental validation based on a dataset from a video platform demonstrates that the proposed framework outperforms single-modality baseline models, with interpretability analyses identifying key factors driving retention, such as content diversity and the frequency of social engagement. This research advances user retention management by bridging behavioral analytics and user psychology, enabling content-based media platforms to reduce user churn through targeted interventions.