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

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Abstract 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.
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PRISM: A Behavior-Aware Personalized Strategy Model for User Retention Optimization in Multi-Domain Recommendation Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article PRISM: A Behavior-Aware Personalized Strategy Model for User Retention Optimization in Multi-Domain Recommendation Systems Radhika Patil, C R Nirmala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7603841/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Personalized Retention Behavioral Prediction Task Recommendation Fuzzy Logic User Engagement Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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