The Stranger Algorithm: A Novel Diversity-Centric Recommendation Model and New Product Survival in the Big Data Era

The Stranger Algorithm: A Novel Diversity-Centric Recommendation Model and New Product Survival in the Big Data Era | 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 The Stranger Algorithm: A Novel Diversity-Centric Recommendation Model and New Product Survival in the Big Data Era Nazif Tchagafo, Belaid Ahiod, Abderrahmane Ez-zahout This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8286600/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 The advent of Big Data has amplified the challenges faced by traditional Recommender Systems (RSs), particularly concerning product Cold Start and user entrapment within the Filter Bubble. This paper introduces the Stranger Algorithm (STRIDE); a hybrid recommendation model designed to inject measurable diversity and ensure the initial visibility and survival of new products. We define a stranger user based on their Diversity Score(Score Stranger) , calculated from Categorical Entropy, Novelty Propensity, and Anti-Popularity Score. This component is dynamically integrated into a hybrid scoring function. The optimization of the scoring weights(w1,w2,w3 ) is managed by a Recurrent Neural Network (RNN), trained with a weighted loss function to ensure high reliability. Results from the MovieLens 1M dataset of Amazon demonstrate that the RNN-optimized Stranger Algorithm increases the New Product Survival Rate by +133.3%, achieving Precision of 89% and Recall of 90% for targeted Cold Start interactions. Recommender System Cold Start Diversity Serendipity Big Data Deep Learning STRIDE RNN Optimization 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8286600","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557584508,"identity":"62c8fd9f-af0a-4738-84a2-fd163ce5a798","order_by":0,"name":"Nazif 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