A Context-Aware Hybrid Search Framework Integrating LLM Tagging and GPU Acceleration for Enhanced E-Commerce Product Discovery

A Context-Aware Hybrid Search Framework Integrating LLM Tagging and GPU Acceleration for Enhanced E-Commerce Product Discovery | 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 Article A Context-Aware Hybrid Search Framework Integrating LLM Tagging and GPU Acceleration for Enhanced E-Commerce Product Discovery Tsung-Yin Ou, Shashika Dharmasena, Areoll Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6999016/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Traditional keyword-based retail search engines typically struggle to deliver relevant results owing to their reliance on exact matches, which can limit user experience and product discovery. As consumer demands grow, search performance optimization, particularly, throughput and latency, has become crucial. To address such challenges, this study presents a novel hybrid search framework centered around a context-aware large language model (LLM) tag generation mechanism tailored to traditional Chinese and specific market nuances (e.g., Taiwanese brands/trends). The core component is integrated with dense embedding and reranker models, and the entire system leverages GPU-accelerated technologies, such as RAPIDS cuDF for efficient large-scale data handling and the NVIDIA Triton Inference Server for optimized real-time inference, including dense embedding caching and dynamic batching. Results demonstrate the relevance and performance efficiency of the framework. The incorporation of context-aware LLM tags can dramatically improve the search relevance, that is, it can increase the intent-aligned conversion rate from 5.09–98.16% and enable the retrieval of all the relevant items in the specific tests in which a keyword search failed. Moreover, the performance optimization yields substantial gains: RAPIDS Dask-cuDF reduces the data-processing latency by 85.5% compared with CPU-based Pandas, Triton Inference Server improves the model serving throughput by nearly 800% and reduces the latency by 97% versus baseline CUDA execution, Redis caching drastically shortens the cached embedding retrieval time, and the LLM component achieves a 178.33 tokens/sec throughput (benchmarked on the Llama-3.1-8B via NIMS). The optimized search framework is successfully deployed on the 711go e-commerce platform. The framework deployment results in a 50% increase in the customer dwell time and a 40% increase in sales over the 90-day verification period, which confirm the ability of the system to enhance consumer browsing experience considerably and deliver tangible business value through improved search functionality. Physical sciences/Engineering Physical sciences/Mathematics and computing Hybrid Search Framework Dense Embedding Large Language Model (LLM) Performance Index E-commerce Platform Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Nov, 2025 Reviews received at journal 07 Sep, 2025 Reviews received at journal 03 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviews received at journal 15 Aug, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers invited by journal 21 Jul, 2025 Editor invited by journal 08 Jul, 2025 Editor assigned by journal 02 Jul, 2025 Submission checks completed at journal 01 Jul, 2025 First submitted to journal 28 Jun, 2025 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. 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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-6999016","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":540785629,"identity":"228ecf91-1ce9-429a-a8cf-b0fd31ae9313","order_by":0,"name":"Tsung-Yin 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Discovery","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hybrid Search Framework, 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As consumer demands grow, search performance optimization, particularly, throughput and latency, has become crucial. To address such challenges, this study presents a novel hybrid search framework centered around a context-aware large language model (LLM) tag generation mechanism tailored to traditional Chinese and specific market nuances (e.g., Taiwanese brands/trends). The core component is integrated with dense embedding and reranker models, and the entire system leverages GPU-accelerated technologies, such as RAPIDS cuDF for efficient large-scale data handling and the NVIDIA Triton Inference Server for optimized real-time inference, including dense embedding caching and dynamic batching.\u003c/p\u003e\u003cp\u003eResults demonstrate the relevance and performance efficiency of the framework. The incorporation of context-aware LLM tags can dramatically improve the search relevance, that is, it can increase the intent-aligned conversion rate from 5.09\u0026ndash;98.16% and enable the retrieval of all the relevant items in the specific tests in which a keyword search failed. Moreover, the performance optimization yields substantial gains: RAPIDS Dask-cuDF reduces the data-processing latency by 85.5% compared with CPU-based Pandas, Triton Inference Server improves the model serving throughput by nearly 800% and reduces the latency by 97% versus baseline CUDA execution, Redis caching drastically shortens the cached embedding retrieval time, and the LLM component achieves a 178.33 tokens/sec throughput (benchmarked on the Llama-3.1-8B via NIMS).\u003c/p\u003e\u003cp\u003eThe optimized search framework is successfully deployed on the 711go e-commerce platform. The framework deployment results in a 50% increase in the customer dwell time and a 40% increase in sales over the 90-day verification period, which confirm the ability of the system to enhance consumer browsing experience considerably and deliver tangible business value through improved search functionality.\u003c/p\u003e","manuscriptTitle":"A Context-Aware Hybrid Search Framework Integrating LLM Tagging and GPU Acceleration for Enhanced E-Commerce Product Discovery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 07:10:11","doi":"10.21203/rs.3.rs-6999016/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-06T06:36:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-07T05:47:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T10:05:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T18:12:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312583195982282738839541734209400318640","date":"2025-08-28T09:47:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92556605908460597912597018539062027433","date":"2025-08-16T19:12:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-15T18:08:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201264561318986694098468363538864942549","date":"2025-08-15T16:08:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-15T02:48:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79199902902433300835747668456549688337","date":"2025-08-10T12:03:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286830819137569685149880049467917659444","date":"2025-08-10T11:06:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-21T12:48:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-08T09:29:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-02T06:50:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-01T11:39:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-28T16:27:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"672ae730-4a35-4cf8-babc-2282791c0eb2","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57526756,"name":"Physical sciences/Engineering"},{"id":57526757,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-03-05T09:08:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 07:10:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6999016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6999016","identity":"rs-6999016","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}