Adaptive Implicit Feedback Weighting: Addressing Signal Saturation and User Heterogeneity in Collaborative Filtering

Adaptive Implicit Feedback Weighting: Addressing Signal Saturation and User Heterogeneity in Collaborative Filtering | 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 Adaptive Implicit Feedback Weighting: Addressing Signal Saturation and User Heterogeneity in Collaborative Filtering A. E. Shyraliiev, I. O. Pyshnograiev This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9216103/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 Recommender systems increasingly rely on implicit feedback (e.g., clicks, play counts, watch times) due to the scarcity of explicit ratings. However, standard collaborative filtering models, such as Weighted Matrix Factorization, traditionally assume that a user’s preference grows continuously and unboundedly with raw interaction counts. This “one-size-fits-all” assumption ignores the severe distributional skew caused by signal saturation and user heterogeneity, where identical interaction volumes can represent vastly different preference intensities. In this paper, we challenge static scaling assumptions by proposing a suite of dynamic, adaptive confidence weighting strategies designed to stabilize latent representations. We introduce the Power-Lift strategy, a numerically stable generalization of Pointwise Mutual Information that captures the probabilistic surprise of interactions, alongside Robust User-Centric weighting, which normalizes frequencies utilizing local, non-parametric user statistics. Furthermore, we formalize the Saturation Hypothesis, demonstrating that sigmoid-based limits on confidence successfully prevent extreme behavioral outliers from distorting latent space geometries. Through rigorous offline evaluation across five diverse datasets, we show that our adaptive weighting approaches consistently outperform traditional unweighted baselines and standard Information Retrieval scaling methods. Notably, our Power-Lift strategy achieves up to a 54.3% relative improvement in Normalized Discounted Cumulative Gain at rank 20 on the highly skewed Steam dataset. Ultimately, these methods provide a highly scalable, computationally efficient mechanism to boost top-N recommendation accuracy and reduce required model capacity without altering downstream architectural inference latency. Recommender Systems Collaborative Filtering Matrix Factorization Implicit Feedback Adaptive Weighting 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-9216103","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614633999,"identity":"013fdc49-6afc-451e-912e-40d886f01bac","order_by":0,"name":"A. E. 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However, standard collaborative filtering models, such as Weighted Matrix Factorization, traditionally assume that a user\u0026rsquo;s preference grows continuously and unboundedly with raw interaction counts. This \u0026ldquo;one-size-fits-all\u0026rdquo; assumption ignores the severe distributional skew caused by signal saturation and user heterogeneity, where identical interaction volumes can represent vastly different preference intensities. In this paper, we challenge static scaling assumptions by proposing a suite of dynamic, adaptive confidence weighting strategies designed to stabilize latent representations. We introduce the Power-Lift strategy, a numerically stable generalization of Pointwise Mutual Information that captures the probabilistic surprise of interactions, alongside Robust User-Centric weighting, which normalizes frequencies utilizing local, non-parametric user statistics. Furthermore, we formalize the Saturation Hypothesis, demonstrating that sigmoid-based limits on confidence successfully prevent extreme behavioral outliers from distorting latent space geometries. Through rigorous offline evaluation across five diverse datasets, we show that our adaptive weighting approaches consistently outperform traditional unweighted baselines and standard Information Retrieval scaling methods. Notably, our Power-Lift strategy achieves up to a 54.3% relative improvement in Normalized Discounted Cumulative Gain at rank 20 on the highly skewed Steam dataset. Ultimately, these methods provide a highly scalable, computationally efficient mechanism to boost top-N recommendation accuracy and reduce required model capacity without altering downstream architectural inference latency.\u003c/p\u003e","manuscriptTitle":"Adaptive Implicit Feedback Weighting: Addressing Signal Saturation and User Heterogeneity in Collaborative Filtering","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-31 03:10:28","doi":"10.21203/rs.3.rs-9216103/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f97a80c-b8e8-43b7-8f8d-5c7962f0d668","owner":[],"postedDate":"March 31st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-11T12:55:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-31 03:10:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9216103","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9216103","identity":"rs-9216103","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}