Autoregressive Generation Strategies for Top-K Sequential Recommendations

Autoregressive Generation Strategies for Top-K Sequential Recommendations | 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 Autoregressive Generation Strategies for Top-K Sequential Recommendations Anna Volodkevich, Danil Gusak, Anton Klenitskiy, Alexey Vasilev This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5657038/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The goal of modern sequential recommender systems is often formulated in terms of next-item prediction. In this paper, we explore the applicability of transformerbased generative models for the Top-K sequential recommendation task, where the goal is to predict items that a user is likely to interact with in the “near future”. This goal aligns with real-world applications of such models in an offline scenario or as a part of multi-stage recommender pipelines. We explore commonly used autoregressive generation strategies, including greedy decoding, beam search, and temperature sampling, to evaluate their performance for the Top-K sequential recommendation task. In addition, we propose novel Reciprocal Rank Aggregation (RRA) and Relevance Aggregation (RA) generation strategies based on multi-sequence generation with temperature sampling and subsequent aggregation. Experiments on diverse datasets give valuable insights regarding the applicability of commonly used strategies and show that the suggested approaches improve performance on longer time horizons compared to the widely used Top-K prediction approach and single-sequence autoregressive generation strategies. recommender systems sequential recommendations transformers autoregressive generation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Mar, 2025 Reviews received at journal 24 Feb, 2025 Reviewers agreed at journal 17 Feb, 2025 Reviews received at journal 07 Feb, 2025 Reviewers agreed at journal 03 Jan, 2025 Reviewers invited by journal 24 Dec, 2024 Editor assigned by journal 19 Dec, 2024 Submission checks completed at journal 19 Dec, 2024 First submitted to journal 16 Dec, 2024 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. 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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-5657038","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392391428,"identity":"b633bacc-eb3b-487c-b942-c31f1942fd98","order_by":0,"name":"Anna Volodkevich","email":"","orcid":"","institution":"Sber AI Lab","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Volodkevich","suffix":""},{"id":392391429,"identity":"5b5703f7-fb5f-43da-ac02-d64574279246","order_by":1,"name":"Danil Gusak","email":"","orcid":"","institution":"Skolkovo Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Danil","middleName":"","lastName":"Gusak","suffix":""},{"id":392391430,"identity":"7ed0210a-7ead-4780-a669-2c73dc4098e8","order_by":2,"name":"Anton Klenitskiy","email":"","orcid":"","institution":"Sber AI Lab","correspondingAuthor":false,"prefix":"","firstName":"Anton","middleName":"","lastName":"Klenitskiy","suffix":""},{"id":392391431,"identity":"f1c6856f-13f4-409a-ae1f-5597f7a8f145","order_by":3,"name":"Alexey Vasilev","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYDACdhBRACIYGw8wMNiAWAb4tTDD1TA2ALWkkaSFgQGo5TBhLfzNzMekeQwYEvv7Dzcc+Nh2PrF/dvPGxzwM9xIbcGiROMyWbDgDqGXGjcSGgzPbbifOuHOs2HAGQzFOLQyHeQwffDBgMGa4wdhwmOfM7cSGGzlmEh8YEnBqkT/M/+FAAlCL/PmDDYf/nDmXOP9GjvmPBDxaDA7zMIJskTM4kNhwmKHiQOIGoC0M+GwxPMxmDPSLhJwhyC89FcnGG2+kFUvOMEgwxqVF7njzM2meChseufPHHz74YWAnO+9G8sbPPBUJsji9DwEScJYjRCWB2EQB9iSoHQWjYBSMghECAJ2oXoghXN3ZAAAAAElFTkSuQmCC","orcid":"","institution":"Sber AI Lab","correspondingAuthor":true,"prefix":"","firstName":"Alexey","middleName":"","lastName":"Vasilev","suffix":""}],"badges":[],"createdAt":"2024-12-16 23:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5657038/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5657038/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72140279,"identity":"df29b3a3-8b6f-4112-b361-faae5e5cabde","added_by":"auto","created_at":"2024-12-23 06:32:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4477622,"visible":true,"origin":"","legend":"","description":"","filename":"AutoregressiveGenerationStrategies.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5657038/v1_covered_0d7c7516-17dd-4ee5-a40d-fb5eaf2df8f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Autoregressive Generation Strategies for Top-K Sequential Recommendations","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"user-modeling-and-user-adapted-interaction","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"umui","sideBox":"Learn more about [User Modeling and User-Adapted Interaction](http://link.springer.com/journal/11257)","snPcode":"11257","submissionUrl":"https://submission.nature.com/new-submission/11257/3","title":"User Modeling and User-Adapted Interaction","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"recommender systems, sequential recommendations, transformers, autoregressive generation","lastPublishedDoi":"10.21203/rs.3.rs-5657038/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5657038/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe goal of modern sequential recommender systems is often formulated in terms of next-item prediction. 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