Information Compensation Graph Contrastive Learning for Recommendation

Information Compensation Graph Contrastive Learning for Recommendation | 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 Information Compensation Graph Contrastive Learning for Recommendation YunLong Guo, ZhenHai Wang, YuHao Xu, WeiMin Li, ZhiRu Wang, Rong Fan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2870572/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Applying graph convolutional neural networks to collaborative filtering is a novel approach pertaining to recommendation systems currently, which has afforded suitable results. However, certain problems still limit the performance of graph collaborative filtering, such as the data uniformity problem. In other words, the quality of embedding the expression of different data after multiple convolutions is reduced, leading to the decline of push model performance. In this paper, we propose self-supervised contrastive learning using global information compensation of feature embeddings, which can effectively alleviate the problem of data uniformity and improve model robustness. Simultaneously, we also propose a graph convolution method using local cooperative propagation to improve the performance of the recommendation model. This embedding calculation method for local cooperative propagation can maximize the influence of low-layer embedding on high-layer embedding, thereby improving the high-layer embedding uniformity. Experiments show that compared with the baseline, our model exhibits significantly improved performance on the three public datasets. Partially on the ML-1M dataset, the proposed ICCL exhibits a performance improvement of 7.96%, proving that our method is valid and explainable. Recommendation System Collaborative Filtering Graph Neural Network Contrastive Learning Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 25 Feb, 2024 Reviewers invited by journal 25 Feb, 2024 Editor assigned by journal 02 May, 2023 First submitted to journal 27 Apr, 2023 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-2870572","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274908735,"identity":"3ffe1a68-e09d-4c94-a9a8-5222762efabe","order_by":0,"name":"YunLong Guo","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"YunLong","middleName":"","lastName":"Guo","suffix":""},{"id":274908736,"identity":"dd0f3a80-9030-4227-b869-ad897b2d17cc","order_by":1,"name":"ZhenHai Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYLACxgYGBgMGxsYHD8DcBOK1NBskkKiFgU2CKC0Gx88efvlzh02eOfvhtorEtsMM/Ow5Bgw/d+DRciYvzULyTFqxZU9i2w2QFsmeNwaMvWdwazE7kGNmYNh2OHHDAagWgxs5BsyMbXi0nH9jZpDY9j9xw/mHbQUgLfYEtdzIMX5wsO1A4gagFQxgWyQIaLG/8caMsbEtGajlYbNEwrl0HokzzwoO9uLRItmfY/zxZ5sd0GHpDz98KLOW429P3vjgJx4tDKDogDMZ2Rh4QPQBvBoYGJg/INh/CKgdBaNgFIyCEQkAhfZcXJYlLt0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-3251-8862","institution":"Linyi University","correspondingAuthor":true,"prefix":"","firstName":"ZhenHai","middleName":"","lastName":"Wang","suffix":""},{"id":274908737,"identity":"2bbc0f9f-1583-46d1-9f8d-fe153f7bb035","order_by":2,"name":"YuHao Xu","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"YuHao","middleName":"","lastName":"Xu","suffix":""},{"id":274908738,"identity":"3e1e4d55-fb57-48a6-9688-613c5a621c78","order_by":3,"name":"WeiMin Li","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"WeiMin","middleName":"","lastName":"Li","suffix":""},{"id":274908739,"identity":"32dc3cb9-14eb-45f5-9e87-66d879cc1b6b","order_by":4,"name":"ZhiRu Wang","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"ZhiRu","middleName":"","lastName":"Wang","suffix":""},{"id":274908740,"identity":"d73df9c0-d57b-4cb8-95c4-f1188dee37f2","order_by":5,"name":"Rong Fan","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2023-04-28 01:26:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2870572/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2870572/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51693207,"identity":"f24816cc-588c-4fc8-957d-eb4d8d589cb2","added_by":"auto","created_at":"2024-02-27 11:00:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":593585,"visible":true,"origin":"","legend":"","description":"","filename":"ICCLsc.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2870572/v1_covered_83a96cfd-c384-4c8a-b759-ea5d0bd5f577.pdf"}],"financialInterests":"","formattedTitle":"Information Compensation Graph Contrastive Learning for Recommendation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Recommendation System, Collaborative Filtering, Graph Neural Network, Contrastive Learning","lastPublishedDoi":"10.21203/rs.3.rs-2870572/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2870572/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eApplying graph convolutional neural networks to collaborative filtering is a novel approach pertaining to recommendation systems currently, which has afforded suitable results. However, certain problems still limit the performance of graph collaborative filtering, such as the data uniformity problem. In other words, the quality of embedding the expression of different data after multiple convolutions is reduced, leading to the decline of push model performance. In this paper, we propose self-supervised contrastive learning using global information compensation of feature embeddings, which can effectively alleviate the problem of data uniformity and improve model robustness. Simultaneously, we also propose a graph convolution method using local cooperative propagation to improve the performance of the recommendation model. This embedding calculation method for local cooperative propagation can maximize the influence of low-layer embedding on high-layer embedding, thereby improving the high-layer embedding uniformity. Experiments show that compared with the baseline, our model exhibits significantly improved performance on the three public datasets. Partially on the ML-1M dataset, the proposed ICCL exhibits a performance improvement of 7.96%, proving that our method is valid and explainable.\u003c/p\u003e","manuscriptTitle":"Information Compensation Graph Contrastive Learning for Recommendation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-27 10:52:20","doi":"10.21203/rs.3.rs-2870572/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-02-26T03:17:49+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-25T20:18:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-05-02T11:04:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Soft Computing","date":"2023-04-27T21:26:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5c8b248f-925c-4fa1-869c-f12de9966af5","owner":[],"postedDate":"February 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-02-27T10:52:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-27 10:52:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2870572","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2870572","identity":"rs-2870572","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}