PRISM: A Behavior-Aware Personalized Strategy Model for User Retention Optimization in Multi-Domain Recommendation Systems

With the increasing complexity of user behaviour and the rising cost of customer acquisition, digital platforms face significant challenges in sustaining long-term user engagement—particularly during the early stages marked by cold-start conditions. Traditional churn prediction models often fall short in providing actionable strategies for personalized retention, necessitating more adaptive and user-centric solutions. This study proposes PRISM (Personalized Retention-Integrated Strategy Model), a modular architecture designed to bridge behavioural prediction with intelligent task recommendation, ensuring both immediate engagement and sustained user retention. PRISM integrates several core modules: the Retention-Oriented Influence Model (ROIM) captures dynamic social propagation patterns; the Retention-Aware Engagement Model (RAEM) evaluates contextual factors such as location, time, reward relevance, and user interest to estimate task acceptance; the Fuzzy Retention Prediction Model (FRPM) leverages fuzzy logic to interpret engagement stimuli; and the Retention-Oriented Behaviour Estimation (ROBE) forecasts user interaction trends. These components work cohesively within the Personalized Fuzzy Engagement Recommendation (PFER) framework to allocate tasks tailored for maximum retention impact. The proposed system is validated across three benchmark datasets—IBM Telco, iQIYI, and MovieLens—using comprehensive evaluation metrics including BLEU, ROUGE, NDCG@10, HR@10 for prediction accuracy, and MB-URS, SB-URS, IUR, NRC for retention performance. Experimental results demonstrate that PRISM consistently surpasses state-of-the-art baselines, establishing a robust, explainable, and domain-neutral strategy for retention-oriented task recommendation and user engagement.