REPREC: Representation Driven Parameter-Efficient Recommendation System

[2607.24845] REPREC: Representation Driven Parameter-Efficient Recommendation System Skip to main content Search Submit Donate Log in Computer Science > Information Retrieval arXiv:2607.24845 (cs) [Submitted on 24 Jul 2026 ( v1 ), last revised 28 Aug 2026 (this version, v4)] Title: REPREC: Representation Driven Parameter-Efficient Recommendation System Authors: Harshini Kavuru , Dwipam Katariya , Giri Iyengar , Pranab Mohanty , Kalanand Mishra , Raghu Machiraju View PDF HTML (experimental) Abstract: Large language models (LLMs) have been applied to sequential recommendation by incorporating collaborative signals through input conditioning or model adaptation. However, existing approaches often require LLM fine-tuning, additional architectural modules, representation distillation, or item-level conditioning over long interaction histories, increasing computational and deployment costs. We propose REPREC, a lightweight framework that conditions a frozen LLM using compact user-level representations. REPREC maps a fixed-size embedding from a frozen sequential encoder into a small set of learned soft tokens through an MLP injector, training only the injector while leaving both pretrained backbones unchanged. Our extensive experiments demonstrate that REPREC consistently improves recommendation performance across different sequential encoders, LLM backbones, and user activity levels. Its compact conditioning mechanism also makes REPREC computationally efficient during both training and inference. Moreover, training with short histories while evaluating with longer contexts retains 94--99\% of full-history performance while achieving an average $1.50\times$ per-epoch training speedup. The code is available at: this https URL Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2607.24845 [cs.IR] (or arXiv:2607.24845v4 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.24845 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Harshini Kavuru [ view email ] [v1] Fri, 24 Jul 2026 22:45:33 UTC (964 KB) [v2] Thu, 30 Jul 2026 11:45:04 UTC (964 KB) [v3] Thu, 27 Aug 2026 15:38:34 UTC (996 KB) [v4] Fri, 28 Aug 2026 01:21:34 UTC (996 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2026-07 Change to browse by: cs cs.AI References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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